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ORCIDJohan A. K. Suykens
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- affiliation: KU Leuven, Department of Electrical Engineering, Belgium
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Books and Theses
- 2002
[b2]Johan A. K. Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle:
Least Squares Support Vector Machines. World Scientific 2002, ISBN 978-981-238-151-4, pp. 1-308- 1996
- [b1]Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Artificial neural networks for modelling and control of non-linear systems. Kluwer 1996, ISBN 978-0-7923-9678-9, pp. I-XII, 1-235
Journal Articles
- 2024
- [j196]Joran Michiels
, Johan A. K. Suykens, Maarten De Vos:
Explaining the model and feature dependencies by decomposition of the Shapley value. Decis. Support Syst. 182: 114234 (2024) - [j195]Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Tensor-based multi-view spectral clustering via shared latent space. Inf. Fusion 108: 102405 (2024) - [j194]Francesco Tonin, Qinghua Tao, Panagiotis Patrinos, Johan A. K. Suykens:
Deep Kernel Principal Component Analysis for multi-level feature learning. Neural Networks 170: 578-595 (2024) - [j193]Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens:
Compressing Features for Learning With Noisy Labels. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2124-2138 (2024) - 2023
- [j192]Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens:
Multi-view kernel PCA for time series forecasting. Neurocomputing 554: 126639 (2023) - [j191]Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens:
Learning With Asymmetric Kernels: Least Squares and Feature Interpretation. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 10044-10054 (2023) - [j190]Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens:
Jigsaw-ViT: Learning jigsaw puzzles in vision transformer. Pattern Recognit. Lett. 166: 53-60 (2023) - [j189]Konstantinos Theodorakos, Oscar Mauricio Agudelo, Joachim Schreurs, Johan A. K. Suykens, Bart De Moor:
Island Transpeciation: A Co-Evolutionary Neural Architecture Search, Applied to Country-Scale Air-Quality Forecasting. IEEE Trans. Evol. Comput. 27(4): 878-892 (2023) - 2022
- [j188]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Nyström landmark sampling and regularized Christoffel functions. Mach. Learn. 111(6): 2213-2254 (2022) - [j187]Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Disentangled Representation Learning and Generation With Manifold Optimization. Neural Comput. 34(10): 2009-2036 (2022) - [j186]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 7128-7148 (2022) - [j185]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7975-7988 (2022) - [j184]Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens:
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions. SIAM J. Math. Data Sci. 4(1): 153-178 (2022) - [j183]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems. SIAM J. Math. Data Sci. 4(3): 1171-1190 (2022) - [j182]Qinghua Tao, Zhen Li, Jun Xu, Shu Lin, Bart De Schutter, Johan A. K. Suykens:
Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network. IEEE Trans. Intell. Transp. Syst. 23(9): 15616-15628 (2022) - [j181]Qinghua Tao, Jun Xu, Zhen Li, Na Xie, Shuning Wang, Xiaoli Li, Johan A. K. Suykens:
Toward Deep Adaptive Hinging Hyperplanes. IEEE Trans. Neural Networks Learn. Syst. 33(11): 6373-6387 (2022) - 2021
- [j180]Qinghua Tao, Zhen Li, Jun Xu, Na Xie, Shuning Wang, Johan A. K. Suykens:
Learning with continuous piecewise linear decision trees. Expert Syst. Appl. 168: 114214 (2021) - [j179]Xin Ma, Mei Xie, Johan A. K. Suykens:
A novel neural grey system model with Bayesian regularization and its applications. Neurocomputing 456: 61-75 (2021) - [j178]Lynn Houthuys, Johan A. K. Suykens:
Tensor-based restricted kernel machines for multi-view classification. Inf. Fusion 68: 54-66 (2021) - [j177]Kevin Villalobos, Johan A. K. Suykens, Arantza Illarramendi:
A flexible alarm prediction system for smart manufacturing scenarios following a forecaster-analyzer approach. J. Intell. Manuf. 32(5): 1323-1344 (2021) - [j176]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Generalization Properties of hyper-RKHS and its Applications. J. Mach. Learn. Res. 22: 140:1-140:38 (2021) - [j175]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. Mach. Learn. 110(6): 1145-1173 (2021) - [j174]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning. Neural Networks 135: 177-191 (2021) - [j173]Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. Neural Networks 142: 661-679 (2021) - [j172]Joachim Schreurs, Iwein Vranckx, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw:
Outlier detection in non-elliptical data by kernel MRCD. Stat. Comput. 31(5): 66 (2021) - [j171]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Diversity Sampling is an Implicit Regularization for Kernel Methods. SIAM J. Math. Data Sci. 3(1): 280-297 (2021) - 2020
- [j170]Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A. K. Suykens, Shuning Wang:
Efficient hinging hyperplanes neural network and its application in nonlinear system identification. Autom. 116: 108906 (2020) - [j169]Yunlong Feng, Jun Fan, Johan A. K. Suykens:
A Statistical Learning Approach to Modal Regression. J. Mach. Learn. Res. 21: 2:1-2:35 (2020) - [j168]Zahra Karevan, Johan A. K. Suykens:
Transductive LSTM for time-series prediction: An application to weather forecasting. Neural Networks 125: 1-9 (2020) - [j167]Fanghui Liu, Xiaolin Huang, Lei Shi, Jie Yang, Johan A. K. Suykens:
A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. IEEE Trans. Neural Networks Learn. Syst. 31(8): 2965-2979 (2020) - 2019
- [j166]Sundaravelpandian Singaravel, Johan A. K. Suykens, Philipp Geyer:
Deep convolutional learning for general early design stage prediction models. Adv. Eng. Informatics 42 (2019) - [j165]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Robust classification of graph-based data. Data Min. Knowl. Discov. 33(1): 230-251 (2019) - [j164]Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Johan A. K. Suykens:
Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification. Int. J. Control 92(4): 908-925 (2019) - [j163]Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A. K. Suykens:
Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization. J. Mach. Learn. Res. 20: 161:1-161:44 (2019) - [j162]Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens:
Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure. IEEE Trans. Neural Networks Learn. Syst. 30(3): 765-776 (2019) - 2018
- [j161]Sundaravelpandian Singaravel, Johan A. K. Suykens, Philipp Geyer:
Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction. Adv. Eng. Informatics 38: 81-90 (2018) - [j160]Giulio Bottegal, Ricardo Castro-Garcia, Johan A. K. Suykens:
A two-experiment approach to Wiener system identification. Autom. 93: 282-289 (2018) - [j159]Yuning Yang, Yunlong Feng, Johan A. K. Suykens:
Correntropy Based Matrix Completion. Entropy 20(3): 171 (2018) - [j158]Zahra Karevan, Johan A. K. Suykens:
Transductive Feature Selection Using Clustering-Based Sample Entropy for Temperature Prediction in Weather Forecasting. Entropy 20(4): 264 (2018) - [j157]Ricardo Castro-Garcia, Koen Tiels, Oscar Mauricio Agudelo, Johan A. K. Suykens:
Hammerstein system identification through best linear approximation inversion and regularisation. Int. J. Control 91(8): 1757-1773 (2018) - [j156]Lynn Houthuys, Rocco Langone, Johan A. K. Suykens:
Multi-View Least Squares Support Vector Machines Classification. Neurocomputing 282: 78-88 (2018) - [j155]Siamak Mehrkanoon, Johan A. K. Suykens:
Deep hybrid neural-kernel networks using random Fourier features. Neurocomputing 298: 46-54 (2018) - [j154]Xiaolin Huang, Lei Shi, Ming Yan, Johan A. K. Suykens:
Pinball loss minimization for one-bit compressive sensing: Convex models and algorithms. Neurocomputing 314: 275-283 (2018) - [j153]Carlos M. Alaíz, Johan A. K. Suykens:
Modified Frank-Wolfe algorithm for enhanced sparsity in support vector machine classifiers. Neurocomputing 320: 47-59 (2018) - [j152]Lynn Houthuys, Rocco Langone, Johan A. K. Suykens:
Multi-View Kernel Spectral Clustering. Inf. Fusion 44: 46-56 (2018) - [j151]Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens:
Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res. 19: 35:1-35:49 (2018) - [j150]Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens:
Indefinite kernel spectral learning. Pattern Recognit. 78: 144-153 (2018) - [j149]Bertrand Gauthier, Johan A. K. Suykens:
Optimal Quadrature-Sparsification for Integral Operator Approximation. SIAM J. Sci. Comput. 40(5): A3636-A3674 (2018) - [j148]Xiaolin Huang, Johan A. K. Suykens, Shuning Wang, Joachim Hornegger, Andreas K. Maier:
Classification With Truncated $\ell _{1}$ Distance Kernel. IEEE Trans. Neural Networks Learn. Syst. 29(5): 2025-2030 (2018) - [j147]Siamak Mehrkanoon, Johan A. K. Suykens:
Regularized Semipaired Kernel CCA for Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 29(7): 3199-3213 (2018) - [j146]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Convex Formulation for Kernel PCA and Its Use in Semisupervised Learning. IEEE Trans. Neural Networks Learn. Syst. 29(8): 3863-3869 (2018) - [j145]Zhongming Chen, Kim Batselier, Johan A. K. Suykens, Ngai Wong:
Parallelized Tensor Train Learning of Polynomial Classifiers. IEEE Trans. Neural Networks Learn. Syst. 29(10): 4621-4632 (2018) - 2017
- [j144]Rocco Langone, Johan A. K. Suykens:
Fast kernel spectral clustering. Neurocomputing 268: 27-33 (2017) - [j143]Rocco Langone, Johan A. K. Suykens:
Supervised aggregated feature learning for multiple instance classification. Inf. Sci. 375: 234-245 (2017) - [j142]Johan A. K. Suykens:
Deep Restricted Kernel Machines Using Conjugate Feature Duality. Neural Comput. 29(8): 2123-2163 (2017) - [j141]Haibo He, Robert Haas, Jun Fu, Barbara Hammer, Daniel W. C. Ho, Fakhri Karray, Dhireesha Kudithipudi, José Antonio Lozano, Teresa Bernarda Ludermir, Jacek Mandziuk, Stefano Melacci, Antonio Paiva, Hong Qiao, Alain Rakotomamonjy, Shiliang Sun, Johan A. K. Suykens, Meng Wang:
Editorial: A Successful Year and Looking Forward to 2017 and Beyond. IEEE Trans. Neural Networks Learn. Syst. 28(1): 2-7 (2017) - [j140]Xiaolin Huang, Lei Shi, Johan A. K. Suykens:
Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values. IEEE Trans. Neural Networks Learn. Syst. 28(7): 1584-1593 (2017) - 2016
- [j139]Stien Heremans, Johan A. K. Suykens, Jos Van Orshoven:
The effect of imposing 'fractional abundance constraints' onto the multilayer perceptron for sub-pixel land cover classification. Int. J. Appl. Earth Obs. Geoinformation 44: 226-238 (2016) - [j138]Siamak Mehrkanoon, Yuri A. W. Shardt, Johan A. K. Suykens, Steven X. Ding:
Estimating the unknown time delay in chemical processes. Eng. Appl. Artif. Intell. 55: 219-230 (2016) - [j137]Rocco Langone, Marc Van Barel, Johan A. K. Suykens:
Entropy-Based Incomplete Cholesky Decomposition for a Scalable Spectral Clustering Algorithm: Computational Studies and Sensitivity Analysis. Entropy 18(5): 182 (2016) - [j136]Vilen Jumutc, Johan A. K. Suykens:
Reweighted stochastic learning. Neurocomputing 198: 135-147 (2016) - [j135]Emanuele Frandi, Ricardo Ñanculef, Stefano Lodi, Claudio Sartori, Johan A. K. Suykens:
Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee. Mach. Learn. 104(2-3): 195-221 (2016) - [j134]Yunlong Feng, Shao-Gao Lv, Hanyuan Hang, Johan A. K. Suykens:
Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery. Neural Comput. 28(3): 525-562 (2016) - [j133]Yunlong Feng, Yuning Yang, Xiaolin Huang, Siamak Mehrkanoon, Johan A. K. Suykens:
Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses. Neural Comput. 28(6): 1217-1247 (2016) - [j132]Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens:
Learning Theory Estimates with Observations from General Stationary Stochastic Processes. Neural Comput. 28(12): 2853-2889 (2016) - [j131]Xiangming Xi, Xiaolin Huang, Johan A. K. Suykens, Shuning Wang:
Coordinate Descent Algorithm for Ramp Loss Linear Programming Support Vector Machines. Neural Process. Lett. 43(3): 887-903 (2016) - [j130]Rocco Langone, Marc Van Barel, Johan A. K. Suykens:
Efficient evolutionary spectral clustering. Pattern Recognit. Lett. 84: 78-84 (2016) - [j129]Yuning Yang, Yunlong Feng, Xiaolin Huang, Johan A. K. Suykens:
Rank-1 Tensor Properties with Applications to a Class of Tensor Optimization Problems. SIAM J. Optim. 26(1): 171-196 (2016) - [j128]Yunlong Feng, Yuning Yang, Johan A. K. Suykens:
Robust Gradient Learning With Applications. IEEE Trans. Neural Networks Learn. Syst. 27(4): 822-835 (2016) - [j127]Yuning Yang, Yunlong Feng, Johan A. K. Suykens:
Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator. IEEE Trans. Neural Networks Learn. Syst. 27(9): 1933-1946 (2016) - 2015
- [j126]Rocco Langone, Carlos Alzate, Bart De Ketelaere, Jonas Vlasselaer, Wannes Meert, Johan A. K. Suykens:
LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. Eng. Appl. Artif. Intell. 37: 268-278 (2015) - [j125]Xiaolin Huang, Lei Shi, Johan A. K. Suykens:
Sequential minimal optimization for SVM with pinball loss. Neurocomputing 149: 1596-1603 (2015) - [j124]Siamak Mehrkanoon, Johan A. K. Suykens:
Learning solutions to partial differential equations using LS-SVM. Neurocomputing 159: 105-116 (2015) - [j123]Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor:
A robust ensemble approach to learn from positive and unlabeled data using SVM base models. Neurocomputing 160: 73-84 (2015) - [j122]Yunlong Feng, Xiaolin Huang, Lei Shi, Yuning Yang, Johan A. K. Suykens:
Learning with the maximum correntropy criterion induced losses for regression. J. Mach. Learn. Res. 16: 993-1034 (2015) - [j121]Siamak Mehrkanoon, Oscar Mauricio Agudelo, Johan A. K. Suykens:
Incremental multi-class semi-supervised clustering regularized by Kalman filtering. Neural Networks 71: 88-104 (2015) - [j120]Raghvendra Mall, Siamak Mehrkanoon, Johan A. K. Suykens:
Identifying intervals for hierarchical clustering using the Gershgorin circle theorem. Pattern Recognit. Lett. 55: 1-7 (2015) - [j119]Li Li, Xiaolin Huang, Johan A. K. Suykens:
Signal recovery for jointly sparse vectors with different sensing matrices. Signal Process. 108: 451-458 (2015) - [j118]Xiaolin Huang, Yipeng Liu, Lei Shi, Sabine Van Huffel, Johan A. K. Suykens:
Two-level ℓ1 minimization for compressed sensing. Signal Process. 108: 459-475 (2015) - [j117]Yuning Yang, Yunlong Feng, Johan A. K. Suykens:
A Rank-One Tensor Updating Algorithm for Tensor Completion. IEEE Signal Process. Lett. 22(10): 1633-1637 (2015) - [j116]Siamak Mehrkanoon, Carlos Alzate, Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering. IEEE Trans. Neural Networks Learn. Syst. 26(4): 720-733 (2015) - [j115]Raghvendra Mall, Johan A. K. Suykens:
Very Sparse LSSVM Reductions for Large-Scale Data. IEEE Trans. Neural Networks Learn. Syst. 26(5): 1086-1097 (2015) - [j114]Carolina Varon, Carlos Alzate, Johan A. K. Suykens:
Noise Level Estimation for Model Selection in Kernel PCA Denoising. IEEE Trans. Neural Networks Learn. Syst. 26(11): 2650-2663 (2015) - 2014
- [j113]Lei Shi, Xiaolin Huang, Zheng Tian, Johan A. K. Suykens:
Quantile regression with ℓ 1 - regularization and Gaussian kernels. Adv. Comput. Math. 40(2): 517-551 (2014) - [j112]Minta Thomas, Kris De Brabanter, Johan A. K. Suykens, Bart De Moor:
Predicting breast cancer using an expression values weighted clinical classifier. BMC Bioinform. 15: 6603 (2014) - [j111]Siamak Mehrkanoon, Saeid Mehrkanoon, Johan A. K. Suykens:
Parameter estimation of delay differential equations: An integration-free LS-SVM approach. Commun. Nonlinear Sci. Numer. Simul. 19(4): 830-841 (2014) - [j110]Xiaolin Huang, Lei Shi, Johan A. K. Suykens:
Asymmetric least squares support vector machine classifiers. Comput. Stat. Data Anal. 70: 395-405 (2014) - [j109]Xiaolin Huang, Lei Shi, Kristiaan Pelckmans, Johan A. K. Suykens:
Asymmetric v-tube support vector regression. Comput. Stat. Data Anal. 77: 371-382 (2014) - [j108]Rocco Langone, Oscar Mauricio Agudelo, Bart De Moor, Johan A. K. Suykens:
Incremental kernel spectral clustering for online learning of non-stationary data. Neurocomputing 139: 246-260 (2014) - [j107]Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens:
Non-parallel support vector classifiers with different loss functions. Neurocomputing 143: 294-301 (2014) - [j106]Dries Geebelen, Kristof Geebelen, Eddy Truyen, Sam Michiels, Johan A. K. Suykens, Joos Vandewalle, Wouter Joosen:
QoS prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset. Inf. Sci. 268: 397-424 (2014) - [j105]Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor:
EnsembleSVM: a library for ensemble learning using support vector machines. J. Mach. Learn. Res. 15(1): 141-145 (2014) - [j104]Xiaolin Huang, Lei Shi, Johan A. K. Suykens:
Ramp loss linear programming support vector machine. J. Mach. Learn. Res. 15(1): 2185-2211 (2014) - [j103]Marco Signoretto, Dinh Quoc Tran, Lieven De Lathauwer, Johan A. K. Suykens:
Learning with tensors: a framework based on convex optimization and spectral regularization. Mach. Learn. 94(3): 303-351 (2014) - [j102]Xiaolin Huang, Lei Shi, Johan A. K. Suykens:
Support Vector Machine Classifier With Pinball Loss. IEEE Trans. Pattern Anal. Mach. Intell. 36(5): 984-997 (2014) - [j101]Vilen Jumutc, Johan A. K. Suykens:
Multi-Class Supervised Novelty Detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(12): 2510-2523 (2014) - [j100]Xuyang Lou, Johan A. K. Suykens:
Hybrid Coupled Local Minimizers. IEEE Trans. Circuits Syst. I Regul. Pap. 61-I(2): 542-551 (2014) - [j99]Anna Marconato, Jonas Sjöberg, Johan A. K. Suykens, Johan Schoukens:
Improved Initialization for Nonlinear State-Space Modeling. IEEE Trans. Instrum. Meas. 63(4): 972-980 (2014) - 2013
- [j98]Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Kernel Spectral Clustering for Big Data Networks. Entropy 15(5): 1567-1586 (2013) - [j97]Marin Matijas, Johan A. K. Suykens, Slavko Krajcar:
Load forecasting using a multivariate meta-learning system. Expert Syst. Appl. 40(11): 4427-4437 (2013) - [j96]Vanya Van Belle, Patrick Neven, Vernon Harvey, Sabine Van Huffel, Johan A. K. Suykens, Stephen P. Boyd:
Risk group detection and survival function estimation for interval coded survival methods. Neurocomputing 112: 200-210 (2013) - [j95]Xiaolin Huang, Siamak Mehrkanoon, Johan A. K. Suykens:
Support vector machines with piecewise linear feature mapping. Neurocomputing 117: 118-127 (2013) - [j94]Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
FURS: Fast and Unique Representative Subset selection retaining large-scale community structure. Soc. Netw. Anal. Min. 3(4): 1075-1095 (2013) - [j93]Xuyang Lou, Johan A. K. Suykens:
Stability of Coupled Local Minimizers Within the Lagrange Programming Network Framework. IEEE Trans. Circuits Syst. I Regul. Pap. 60-I(2): 377-388 (2013) - [j92]Xiaolin Huang, Marin Matijas, Johan A. K. Suykens:
Hinging Hyperplanes for Time-Series Segmentation. IEEE Trans. Neural Networks Learn. Syst. 24(8): 1279-1291 (2013) - 2012
- [j91]Siamak Mehrkanoon, Johan A. K. Suykens:
LS-SVM approximate solution to linear time varying descriptor systems. Autom. 48(10): 2502-2511 (2012) - [j90]Jan Luts, Geert Molenberghs, Geert Verbeke, Sabine Van Huffel, Johan A. K. Suykens:
A mixed effects least squares support vector machine model for classification of longitudinal data. Comput. Stat. Data Anal. 56(3): 611-628 (2012) - [j89]Adrien Combaz, Nikolay Chumerin, Nikolay V. Manyakov, Arne Robben, Johan A. K. Suykens, Marc M. Van Hulle:
Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface. Neurocomputing 80: 73-82 (2012) - [j88]Carlos Alzate, Johan A. K. Suykens:
Hierarchical kernel spectral clustering. Neural Networks 35: 21-30 (2012) - [j87]Shi Yu, Léon-Charles Tranchevent, Xinhai Liu, Wolfgang Glänzel, Johan A. K. Suykens, Bart De Moor, Yves Moreau:
Optimized Data Fusion for Kernel k-Means Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 34(5): 1031-1039 (2012) - [j86]Kris De Brabanter, Peter Karsmakers, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Confidence bands for least squares support vector machine classifiers: A regression approach. Pattern Recognit. 45(6): 2280-2287 (2012) - [j85]Devy Widjaja, Carolina Varon, Alexander Dorado, Johan A. K. Suykens, Sabine Van Huffel:
Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration. IEEE Trans. Biomed. Eng. 59(4): 1169-1176 (2012) - [j84]Dries Geebelen, Johan A. K. Suykens, Joos Vandewalle:
Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation. IEEE Trans. Neural Networks Learn. Syst. 23(4): 682-688 (2012) - [j83]Siamak Mehrkanoon, Tillmann Falck, Johan A. K. Suykens:
Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines. IEEE Trans. Neural Networks Learn. Syst. 23(9): 1356-1367 (2012) - [j82]Marco Signoretto, Emanuele Olivetti, Lieven De Lathauwer, Johan A. K. Suykens:
Classification of Multichannel Signals With Cumulant-Based Kernels. IEEE Trans. Signal Process. 60(5): 2304-2314 (2012) - 2011
- [j81]Vanya Van Belle, Kristiaan Pelckmans, Sabine Van Huffel, Johan A. K. Suykens:
Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artif. Intell. Medicine 53(2): 107-118 (2011) - [j80]Vanya Van Belle, Kristiaan Pelckmans, Sabine Van Huffel, Johan A. K. Suykens:
Improved performance on high-dimensional survival data by application of Survival-SVM. Bioinform. 27(1): 87-94 (2011) - [j79]Shi Yu, Xinhai Liu, Léon-Charles Tranchevent, Wolfgang Glänzel, Johan A. K. Suykens, Bart De Moor, Yves Moreau:
Optimized data fusion for K-means Laplacian clustering. Bioinform. 27(1): 118-126 (2011) - [j78]Geert J. Postma, Jan Luts, Albert J. Idema, Margarida Julià-Sapé, Àngel Moreno-Torres, Witek Gajewicz, Johan A. K. Suykens, Arend Heerschap, Sabine Van Huffel, Lutgarde M. C. Buydens:
On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation. Comput. Biol. Medicine 41(2): 87-97 (2011) - [j77]Carlos Alzate, Johan A. K. Suykens:
Sparse kernel spectral clustering models for large-scale data analysis. Neurocomputing 74(9): 1382-1390 (2011) - [j76]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
Learning Transformation Models for Ranking and Survival Analysis. J. Mach. Learn. Res. 12: 819-862 (2011) - [j75]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Kernel Regression in the Presence of Correlated Errors. J. Mach. Learn. Res. 12: 1955-1976 (2011) - [j74]Peter Karsmakers, Kristiaan Pelckmans, Kris De Brabanter, Hugo Van hamme, Johan A. K. Suykens:
Sparse conjugate directions pursuit with application to fixed-size kernel models. Mach. Learn. 85(1-2): 109-148 (2011) - [j73]Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens:
A kernel-based framework to tensorial data analysis. Neural Networks 24(8): 861-874 (2011) - [j72]Jorge López Lázaro, Johan A. K. Suykens:
First and Second Order SMO Algorithms for LS-SVM Classifiers. Neural Process. Lett. 33(1): 31-44 (2011) - [j71]Marco Signoretto, Raf Van de Plas, Bart De Moor, Johan A. K. Suykens:
Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data. IEEE Signal Process. Lett. 18(7): 403-406 (2011) - [j70]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression. IEEE Trans. Neural Networks 22(1): 110-120 (2011) - 2010
- [j69]Shi Yu, Tillmann Falck, Anneleen Daemen, Léon-Charles Tranchevent, Johan A. K. Suykens, Bart De Moor, Yves Moreau:
L2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinform. 11: 309 (2010) - [j68]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Optimized fixed-size kernel models for large data sets. Comput. Stat. Data Anal. 54(6): 1484-1504 (2010) - [j67]Michiel Debruyne, Andreas Christmann, Mia Hubert, Johan A. K. Suykens:
Robustness of reweighted Least Squares Kernel Based Regression. J. Multivar. Anal. 101(2): 447-463 (2010) - [j66]Carlos Alzate, Johan A. K. Suykens:
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA. IEEE Trans. Pattern Anal. Mach. Intell. 32(2): 335-347 (2010) - [j65]Samuel Xavier de Souza, Johan A. K. Suykens, Joos Vandewalle, Désiré Bollé:
Coupled Simulated Annealing. IEEE Trans. Syst. Man Cybern. Part B 40(2): 320-335 (2010) - [j64]Paschalis Tsiaflakis, Ion Necoara, Johan A. K. Suykens, Marc Moonen:
Improved dual decomposition based optimization for DSL dynamic spectrum management. IEEE Trans. Signal Process. 58(4): 2230-2245 (2010) - 2009
- [j63]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Least conservative support and tolerance tubes. IEEE Trans. Inf. Theory 55(8): 3799-3806 (2009) - 2008
- [j62]Lars Imsland, J. Anthony Rossiter, Bert Pluymers, Johan A. K. Suykens:
Robust triple mode MPC. Int. J. Control 81(4): 679-689 (2008) - [j61]Carlos Alzate, Johan A. K. Suykens:
A regularized kernel CCA contrast function for ICA. Neural Networks 21(2-3): 170-181 (2008) - [j60]Fabian Ojeda, Johan A. K. Suykens, Bart De Moor:
Low rank updated LS-SVM classifiers for fast variable selection. Neural Networks 21(2-3): 437-449 (2008) - [j59]Ion Necoara, Johan A. K. Suykens:
Application of a Smoothing Technique to Decomposition in Convex Optimization. IEEE Trans. Autom. Control. 53(11): 2674-2679 (2008) - [j58]Johan A. K. Suykens:
Data Visualization and Dimensionality Reduction Using Kernel Maps With a Reference Point. IEEE Trans. Neural Networks 19(9): 1501-1517 (2008) - [j57]Carlos Alzate, Johan A. K. Suykens:
Kernel Component Analysis Using an Epsilon-Insensitive Robust Loss Function. IEEE Trans. Neural Networks 19(9): 1583-1598 (2008) - 2007
- [j56]Jan Luts, Arend Heerschap, Johan A. K. Suykens, Sabine Van Huffel:
A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. Artif. Intell. Medicine 40(2): 87-102 (2007) - [j55]Dániel Hillier, Serkan Günel, Johan A. K. Suykens, Joos Vandewalle:
Partial Synchronization in oscillator Arrays with Asymmetric Coupling. Int. J. Bifurc. Chaos 17(11): 4177-4185 (2007) - [j54]Luc Hoegaerts, Lieven De Lathauwer, Ivan Goethals, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Efficiently updating and tracking the dominant kernel principal components. Neural Networks 20(2): 220-229 (2007) - [j53]Chuan Lu, Andy Devos, Johan A. K. Suykens, Carles Arús, Sabine Van Huffel:
Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis. IEEE Trans. Inf. Technol. Biomed. 11(3): 338-347 (2007) - [j52]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
A Convex Approach to Validation-Based Learning of the Regularization Constant. IEEE Trans. Neural Networks 18(3): 917-920 (2007) - 2006
- [j51]Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor:
Fixed-size Least Squares Support Vector Machines: A Large Scale Application in Electrical Load Forecasting. Comput. Manag. Sci. 3(2): 113-129 (2006) - [j50]Tony Van Gestel, Bart Baesens, Peter Van Dijcke, Joao Garcia, Johan A. K. Suykens, Jan Vanthienen:
A process model to develop an internal rating system: Sovereign credit ratings. Decis. Support Syst. 42(2): 1131-1151 (2006) - [j49]Tony Van Gestel, Bart Baesens, Johan A. K. Suykens, Dirk Van den Poel, Dirk-Emma Baestaens, Marleen Willekens:
Bayesian kernel based classification for financial distress detection. Eur. J. Oper. Res. 172(3): 979-1003 (2006) - [j48]Müstak E. Yalçin, Johan A. K. Suykens:
Spatiotemporal Pattern Formation on the ACE16K CNN Chip. Int. J. Bifurc. Chaos 16(5): 1537-1546 (2006) - [j47]Samuel Xavier de Souza, Johan A. K. Suykens, Joos Vandewalle:
Learning of spatiotemporal behaviour in cellular neural networks. Int. J. Circuit Theory Appl. 34(1): 127-140 (2006) - [j46]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods. Mach. Learn. 62(3): 217-252 (2006) - 2005
- [j45]Bert Pluymers, L. Roobrouck, J. Buijs, Johan A. K. Suykens, Bart De Moor:
Constrained linear MPC with time-varying terminal cost using convex combinations. Autom. 41(5): 831-837 (2005) - [j44]Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Identification of MIMO Hammerstein models using least squares support vector machines. Autom. 41(7): 1263-1272 (2005) - [j43]Nathalie Pochet, Frizo A. L. Janssens, Frank De Smet, Kathleen Marchal, Johan A. K. Suykens, Bart De Moor:
M@CBETH: a microarray classification benchmarking tool. Bioinform. 21(14): 3185-3186 (2005) - [j42]Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Subset based least squares subspace regression in RKHS. Neurocomputing 63: 293-323 (2005) - [j41]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Building sparse representations and structure determination on LS-SVM substrates. Neurocomputing 64: 137-159 (2005) - [j40]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
The differogram: Non-parametric noise variance estimation and its use for model selection. Neurocomputing 69(1-3): 100-122 (2005) - [j39]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Handling missing values in support vector machine classifiers. Neural Networks 18(5-6): 684-692 (2005) - [j38]Kristiaan Pelckmans, Marcelo Espinoza, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Primal-Dual Monotone Kernel Regression. Neural Process. Lett. 22(2): 171-182 (2005) - [j37]Bert Pluymers, Johan A. K. Suykens, Bart De Moor:
Min-max feedback MPC using a time-varying terminal constraint set and comments on "Efficient robust constrained model predictive control with a time-varying terminal constraint set". Syst. Control. Lett. 54(12): 1143-1148 (2005) - [j36]Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Subspace identification of Hammerstein systems using least squares support vector machines. IEEE Trans. Autom. Control. 50(10): 1509-1519 (2005) - [j35]Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor:
Kernel based partially linear models and nonlinear identification. IEEE Trans. Autom. Control. 50(10): 1602-1606 (2005) - 2004
- [j34]Lukas Lukas, Andy Devos, Johan A. K. Suykens, Leentje Vanhamme, Franklyn A. Howe, Carles Majós, Àngel Moreno-Torres, M. Van Der Graaf, Anne Rosemary Tate, Carles Arús, Sabine Van Huffel:
Brain tumor classification based on long echo proton MRS signals. Artif. Intell. Medicine 31(1): 73-89 (2004) - [j33]Nathalie Pochet, Frank De Smet, Johan A. K. Suykens, Bart De Moor:
Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinform. 20(17): 3185-3195 (2004) - [j32]Tony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle:
Benchmarking Least Squares Support Vector Machine Classifiers. Mach. Learn. 54(1): 5-32 (2004) - [j31]Samuel Xavier de Souza, Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle:
Toward CNN chip-specific robustness. IEEE Trans. Circuits Syst. I Regul. Pap. 51-I(5): 892-902 (2004) - [j30]Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle:
True random bit generation from a double-scroll attractor. IEEE Trans. Circuits Syst. I Regul. Pap. 51-I(7): 1395-1404 (2004) - 2003
- [j29]Chuan Lu, Tony Van Gestel, Johan A. K. Suykens, Sabine Van Huffel, Ignace Vergote, Dirk Timmerman:
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines. Artif. Intell. Medicine 28(3): 281-306 (2003) - [j28]Bart Baesens, Tony Van Gestel, Stijn Viaene, M. Stepanova, Johan A. K. Suykens, Jan Vanthienen:
Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. 54(6): 627-635 (2003) - [j27]Ivan Goethals, Tony Van Gestel, Johan A. K. Suykens, Paul Van Dooren, Bart De Moor:
Identification of positive real models in subspace identification by using regularization. IEEE Trans. Autom. Control. 48(10): 1843-1847 (2003) - [j26]Johan A. K. Suykens, Tony Van Gestel, Joos Vandewalle, Bart De Moor:
A support vector machine formulation to PCA analysis and its kernel version. IEEE Trans. Neural Networks 14(2): 447-450 (2003) - 2002
- [j25]Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle, Serdar Özoguz:
Families of scroll Grid attractors. Int. J. Bifurc. Chaos 12(1): 23-41 (2002) - [j24]Johan A. K. Suykens, Jos De Brabanter, Lukas Lukas, Joos Vandewalle:
Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1-4): 85-105 (2002) - [j23]Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle:
Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis. Neural Comput. 14(5): 1115-1147 (2002) - [j22]Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle:
Multiclass LS SVMs Moderated Outputs and Coding Decoding Schemes. Neural Process. Lett. 15(1): 45-58 (2002) - 2001
- [j21]Johan A. K. Suykens:
Support Vector Machines: A Nonlinear Modelling and Control Perspective. Eur. J. Control 7(2-3): 311-327 (2001) - [j20]Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle:
Master-Slave Synchronization of Lur'e Systems with Time-Delay. Int. J. Bifurc. Chaos 11(6): 1707-1722 (2001) - [j19]Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Intelligence and Cooperative Search by Coupled Local Minimizers. Int. J. Bifurc. Chaos 11(8): 2133-2144 (2001) - [j18]Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene:
Knowledge discovery in a direct marketing case using least squares support vector machines. Int. J. Intell. Syst. 16(9): 1023-1036 (2001) - [j17]Michel Duhoux, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
Improved Long-Term Temperature Prediction by Chaining of Neural Networks. Int. J. Neural Syst. 11(1): 1-10 (2001) - [j16]Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Optimal control by least squares support vector machines. Neural Networks 14(1): 23-35 (2001) - [j15]Tony Van Gestel, Johan A. K. Suykens, Paul Van Dooren, Bart De Moor:
Identification of stable models in subspace identification by using regularization. IEEE Trans. Autom. Control. 46(9): 1416-1420 (2001) - [j14]Tony Van Gestel, Johan A. K. Suykens, Dirk-Emma Baestaens, Annemie Lambrechts, Gert R. G. Lanckriet, Bruno Vandaele, Bart De Moor, Joos Vandewalle:
Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Trans. Neural Networks 12(4): 809-821 (2001) - 2000
- [j13]Johan A. K. Suykens, Joos Vandewalle:
Chaos Synchronization: a Lagrange Programming Network Approach. Int. J. Bifurc. Chaos 10(4): 797-810 (2000) - [j12]Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
Robust local stability of multilayer recurrent neural networks. IEEE Trans. Neural Networks Learn. Syst. 11(1): 222-229 (2000) - 1999
- [j11]Johan A. K. Suykens, Joos Vandewalle:
Chaos control using least‐squares support vector machines. Int. J. Circuit Theory Appl. 27(6): 605-615 (1999) - [j10]Johan A. K. Suykens, Joos Vandewalle:
Least Squares Support Vector Machine Classifiers. Neural Process. Lett. 9(3): 293-300 (1999) - [j9]Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Lur'e systems with multilayer perceptron and recurrent neural networks: absolute stability and dissipativity. IEEE Trans. Autom. Control. 44(4): 770-774 (1999) - [j8]Johan A. K. Suykens, Joos Vandewalle:
Training multilayer perceptron classifiers based on a modified support vector method. IEEE Trans. Neural Networks 10(4): 907-911 (1999) - 1998
- [j7]Herman Verrelst, Kristel Van Acker, Johan A. K. Suykens, Bart Motmans, Bart De Moor, Joos Vandewalle:
Application of NLq Neural Control Theory to a Ball and Beam System. Eur. J. Control 4(2): 148-157 (1998) - [j6]Johan A. K. Suykens, Herman Verrelst, Joos Vandewalle:
On-Line Learning Fokker-Planck Machine. Neural Process. Lett. 7(2): 81-89 (1998) - 1997
- [j5]Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
NLq Theory: A Neural Control Framework with Global Asymptotic Stability Criteria. Neural Networks 10(4): 615-637 (1997) - [j4]Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
NLq theory: checking and imposing stability of recurrent neural networks for nonlinear modeling. IEEE Trans. Signal Process. 45(11): 2682-2691 (1997) - 1996
- [j3]Johan A. K. Suykens, Joos Vandewalle:
Discrete Time Interconnected Cellular Neural Networks Within NLq Theory. Int. J. Circuit Theory Appl. 24(1): 25-36 (1996) - [j2]Johan A. K. Suykens, Philippe Lemmerling, Wouter Favoreel, Bart De Moor, M. Crepel, P. Briol:
Modelling the Belgian Gas Consumption Using Neural Networks. Neural Process. Lett. 4(3): 157-166 (1996) - 1994
- [j1]Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
Static and dynamic stabilizing neural controllers, applicable to transition between equilibrium points. Neural Networks 7(5): 819-831 (1994)
Conference and Workshop Papers
- 2024
- [c190]Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification. AAAI 2024: 10766-10774 - [c189]Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens:
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes. ICML 2024 - [c188]Qinghua Tao, Francesco Tonin, Alex Lambert, Yingyi Chen, Panagiotis Patrinos, Johan A. K. Suykens:
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method. ICML 2024 - 2023
- [c187]Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool:
Unbalanced Optimal Transport: A Unified Framework for Object Detection. CVPR 2023: 3198-3207 - [c186]Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens:
Tensorized LSSVMS For Multitask Regression. ICASSP 2023: 1-5 - [c185]Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens:
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. ICML 2023: 34379-34393 - [c184]Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens:
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. NeurIPS 2023 - 2022
- [c183]Arun Pandey, Hannes De Meulemeester, Henri De Plaen, Bart De Moor, Johan A. K. Suykens:
Recurrent Restricted Kernel Machines for Time-series Forecasting. ESANN 2022 - [c182]Bram De Cooman, Johan A. K. Suykens, Andreas Ortseifen:
Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies. LOD (2) 2022: 193-218 - [c181]Fanghui Liu, Johan A. K. Suykens, Volkan Cevher:
On the Double Descent of Random Features Models Trained with SGD. NeurIPS 2022 - 2021
- [c180]Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. AISTATS 2021: 388-396 - [c179]Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimensions: Refined analysis beyond double descent. AISTATS 2021: 649-657 - [c178]Brecht Evens, Puya Latafat, Andreas Themelis, Johan A. K. Suykens, Panagiotis Patrinos:
Neural Network Training as an Optimal Control Problem : - An Augmented Lagrangian Approach -. CDC 2021: 5136-5143 - [c177]Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens:
Boosting Co-Teaching With Compression Regularization for Label Noise. CVPR Workshops 2021: 2688-2692 - [c176]Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. IJCNN 2021: 1-8 - [c175]Marcin Orchel, Johan A. K. Suykens:
Improved Update Rule and Sampling of Stochastic Gradient Descent with Extreme Early Stopping for Support Vector Machines. LOD 2021: 147-161 - [c174]Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. LOD 2021: 466-480 - [c173]Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
The Bures Metric for Generative Adversarial Networks. ECML/PKDD (2) 2021: 52-66 - [c172]Johan A. K. Suykens:
Kernel Machines in Time (Invited Talk). TIME 2021: 3:1-3:1 - 2020
- [c171]Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens:
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. AAAI 2020: 4844-4851 - [c170]Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens:
Learning from partially labeled data. ESANN 2020: 493-502 - [c169]Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens:
Wasserstein Exponential Kernels. IJCNN 2020: 1-6 - [c168]Marcin Orchel, Johan A. K. Suykens:
Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent. LOD (2) 2020: 481-493 - [c167]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality. LOD (1) 2020: 613-624 - [c166]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. NeurIPS 2020 - 2019
- [c165]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN 2019 - [c164]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. BNAIC/BENELEARN 2019 - [c163]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. BNAIC/BENELEARN (Selected Papers) 2019: 70-82 - [c162]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN (Selected Papers) 2019: 137-151 - [c161]Marcin Orchel, Johan A. K. Suykens:
Axiomatic Kernels on Graphs for Support Vector Machines. ICANN (Workshop) 2019: 685-700 - 2018
- [c160]Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens:
Solving lp-norm regularization with tensor kernels. AISTATS 2018: 1655-1663 - [c159]Hussain Syed Kazmi, Johan A. K. Suykens, Johan Driesen:
Valuing Knowledge, Information and Agency in Multi-agent Reinforcement Learning: A Case Study in Smart Buildings. AAMAS 2018: 585-587 - [c158]Qinghua Tao, Jun Xu, Johan A. K. Suykens, Shuning Wang:
Fast Adaptive Hinging Hyperplanes. CDC 2018: 1482-1487 - [c157]Siamak Mehrkanoon, Matthew B. Blaschko, Johan A. K. Suykens:
Shallow and Deep Models for Domain Adaptation problems. ESANN 2018 - [c156]Joachim Schreurs, Johan A. K. Suykens:
Generative Kernel PCA. ESANN 2018 - [c155]Lynn Houthuys, Johan A. K. Suykens:
Tensor Learning in Multi-view Kernel PCA. ICANN (2) 2018: 205-215 - [c154]Zahra Karevan, Lynn Houthuys, Johan A. K. Suykens:
Weighted Multi-view Deep Neural Networks for Weather Forecasting. ICANN (3) 2018: 489-499 - 2017
- [c153]Giulio Bottegal, Ricardo Castro-Garcia, Johan A. K. Suykens:
On the identification of Wiener systems with polynomial nonlinearity. CDC 2017: 6475-6480 - [c152]Zahra Karevan, Yunlong Feng, Johan A. K. Suykens:
Moving Least Squares Support Vector Machines for weather temperature prediction. ESANN 2017 - [c151]Siamak Mehrkanoon, Andreas Zell, Johan A. K. Suykens:
Scalable Hybrid Deep Neural Kernel Networks. ESANN 2017 - [c150]Pantelis Sopasakis, Andreas Themelis, Johan A. K. Suykens, Panagiotis Patrinos:
A primal-dual line search method and applications in image processing. EUSIPCO 2017: 1065-1069 - [c149]Giulio Bottegal, Johan A. K. Suykens:
Probabilistic matrix factorization from quantized measurements. IJCNN 2017: 270-277 - [c148]Lynn Houthuys, Zahra Karevan, Johan A. K. Suykens:
Multi-view LS-SVM regression for black-box temperature prediction in weather forecasting. IJCNN 2017: 1102-1108 - [c147]Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Johan A. K. Suykens:
MIMO hammerstein system identification using LS-SVM and steady state time response. SSCI 2017: 1-7 - [c146]Lynn Houthuys, Johan A. K. Suykens:
Unpaired multi-view kernel spectral clustering. SSCI 2017: 1-7 - 2016
- [c145]Rocco Langone, Johan A. K. Suykens:
Efficient multiple scale kernel classifiers. IEEE BigData 2016: 128-133 - [c144]Marcelo Aliquintuy, Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens:
Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso. CIARP 2016: 208-216 - [c143]Lynn Houthuys, Rocco Langone, Johan A. K. Suykens:
Clustering from two data sources using a kernel-based approach with weight coupling. ESANN 2016 - [c142]Zahra Karevan, Johan A. K. Suykens:
Spatio-temporal feature selection for black-box weather forecasting. ESANN 2016 - [c141]Rocco Langone, Raghvendra Mall, Vilen Jumutc, Johan A. K. Suykens:
Fast in-memory spectral clustering using a fixed-size approach. ESANN 2016 - [c140]Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Koen Tiels, Johan A. K. Suykens:
Hammerstein system identification using LS-SVM and steady state time response. ECC 2016: 1063-1068 - [c139]Zahra Karevan, Johan A. K. Suykens:
Clustering-based feature selection for black-box weather temperature prediction. IJCNN 2016: 2722-2729 - [c138]Raghvendra Mall, Halima Bensmail, Rocco Langone, Carolina Varon, Johan A. K. Suykens:
Denoised Kernel Spectral data Clustering. IJCNN 2016: 3709-3716 - [c137]Siamak Mehrkanoon, Johan A. K. Suykens:
Multi-label semi-supervised learning using regularized kernel spectral clustering. IJCNN 2016: 4009-4016 - [c136]Ricardo Castro-Garcia, Johan A. K. Suykens:
Wiener System Identification using Best Linear Approximation within the LS-SVM framework. LA-CCI 2016: 1-6 - [c135]Siamak Mehrkanoon, Johan A. K. Suykens:
Scalable Semi-supervised kernel spectral learning using random Fourier features. SSCI 2016: 1-8 - 2015
- [c134]Vilen Jumutc, Rocco Langone, Johan A. K. Suykens:
Regularized and sparse stochastic k-means for distributed large-scale clustering. IEEE BigData 2015: 2535-2540 - [c133]Ricardo Castro-Garcia, Koen Tiels, Johan Schoukens, Johan A. K. Suykens:
Incorporating Best Linear Approximation within LS-SVM-based Hammerstein System Identification. CDC 2015: 7392-7397 - [c132]Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Ranking Overlap and Outlier Points in Data using Soft Kernel Spectral Clustering. ESANN 2015 - [c131]Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens:
A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification. IJCNN 2015: 1-8 - [c130]Zahra Karevan, Siamak Mehrkanoon, Johan A. K. Suykens:
Black-box modeling for temperature prediction in weather forecasting. IJCNN 2015: 1-8 - [c129]Raghvendra Mall, Johan A. K. Suykens:
Kernel spectral document clustering using unsupervised precision-recall metrics. IJCNN 2015: 1-7 - [c128]Siamak Mehrkanoon, Oscar Mauricio Agudelo, Raghvendra Mall, Johan A. K. Suykens:
Hierarchical semi-supervised clustering using KSC based model. IJCNN 2015: 1-8 - [c127]Mandar Chandorkar, Raghvendra Mall, Oliver Lauwers, Johan A. K. Suykens, Bart De Moor:
Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification. SSCI 2015: 522-528 - 2014
- [c126]Raghvendra Mall, Vilen Jumutc, Rocco Langone, Johan A. K. Suykens:
Representative subsets for big data learning using k-NN graphs. IEEE BigData 2014: 37-42 - [c125]Marco Signoretto, Emanuele Frandi, Zahra Karevan, Johan A. K. Suykens:
High level high performance computing for multitask learning of time-varying models. CIBD 2014: 14-19 - [c124]Rocco Langone, Raghvendra Mall, Johan A. K. Suykens:
Clustering data over time using kernel spectral clustering with memory. CIDM 2014: 1-8 - [c123]Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Agglomerative hierarchical kernel spectral data clustering. CIDM 2014: 9-16 - [c122]Vilen Jumutc, Johan A. K. Suykens:
New bilinear formulation to semi-supervised classification based on Kernel Spectral Clustering. CIDM 2014: 41-47 - [c121]Rocco Langone, Carlos Alzate, Abdellatif Bey-Temsamani, Johan A. K. Suykens:
Alarm prediction in industrial machines using autoregressive LS-SVM models. CIDM 2014: 359-364 - [c120]Charalampos N. Moschopoulos, Dusan Popovic, Rocco Langone, Johan A. K. Suykens, Bart De Moor, Yves Moreau:
Gene interaction networks boost genetic algorithm performance in biomarker discovery. MCDM 2014: 144-149 - [c119]Vilen Jumutc, Johan A. K. Suykens:
Reweighted l1 Dual Averaging Approach for Sparse Stochastic Learning. ESANN 2014 - [c118]Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Agglomerative hierarchical kernel spectral clustering for large scale networks. ESANN 2014 - [c117]Diego Hernán Peluffo-Ordóñez, Carlos Alzate, Johan A. K. Suykens, Germán Castellanos-Domínguez:
Optimal Data Projection for Kernel Spectral Clustering. ESANN 2014 - [c116]Raghvendra Mall, Siamak Mehrkanoon, Rocco Langone, Johan A. K. Suykens:
Optimal reduced sets for sparse kernel spectral clustering. IJCNN 2014: 2436-2443 - [c115]Ricardo Castro-Garcia, Siamak Mehrkanoon, Anna Marconato, Johan Schoukens, Johan A. K. Suykens:
SVD truncation schemes for fixed-size kernel models. IJCNN 2014: 3922-3929 - [c114]Siamak Mehrkanoon, Johan A. K. Suykens:
Large scale semi-supervised learning using KSC based model. IJCNN 2014: 4152-4159 - [c113]Vilen Jumutc, Johan A. K. Suykens:
Reweighted l 2-Regularized Dual Averaging Approach for Highly Sparse Stochastic Learning. ISNN 2014: 232-242 - 2013
- [c112]Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Self-tuned kernel spectral clustering for large scale networks. IEEE BigData 2013: 385-393 - [c111]Rocco Langone, Carlos Alzate, Bart De Ketelaere, Johan A. K. Suykens:
Kernel spectral clustering for predicting maintenance of industrial machines. CIDM 2013: 39-45 - [c110]Vilen Jumutc, Johan A. K. Suykens:
Supervised Novelty Detection. CIDM 2013: 143-149 - [c109]Marco Signoretto, Johan A. K. Suykens:
DynOpt: Incorporating dynamics into mean-variance portfolio optimization. CIFEr 2013: 48-54 - [c108]Raghvendra Mall, Johan A. K. Suykens, Mohammed El Anbari, Halima Bensmail:
Primal-Dual Framework for Feature Selection using Least Squares Support Vector Machines. COMAD 2013: 105-108 - [c107]Carolina Varon, Dries Testelmans, Bertien Buyse, Johan A. K. Suykens, Sabine Van Huffel:
Sleep apnea classification using least-squares support vector machines on single lead ECG. EMBC 2013: 5029-5032 - [c106]Vilen Jumutc, Xiaolin Huang, Johan A. K. Suykens:
Fixed-size Pegasos for hinge and pinball loss SVM. IJCNN 2013: 1-7 - [c105]Rocco Langone, Raghvendra Mall, Johan A. K. Suykens:
Soft kernel spectral clustering. IJCNN 2013: 1-8 - [c104]Siamak Mehrkanoon, Johan A. K. Suykens:
Non-parallel semi-supervised classification based on kernel spectral clustering. IJCNN 2013: 1-8 - [c103]Diego Hernán Peluffo-Ordóñez, Sergio García-Vega, Rocco Langone, Johan A. K. Suykens, Germán Castellanos-Domínguez:
Kernel spectral clustering for dynamic data using multiple kernel learning. IJCNN 2013: 1-6 - [c102]Raghvendra Mall, Johan A. K. Suykens:
Sparse Reductions for Fixed-Size Least Squares Support Vector Machines on Large Scale Data. PAKDD (1) 2013: 161-173 - [c101]Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Highly Sparse Reductions to Kernel Spectral Clustering. PReMI 2013: 163-169 - [c100]Vilen Jumutc, Johan A. K. Suykens:
Weighted Coordinate-Wise Pegasos. PReMI 2013: 262-269 - [c99]Karen Vanderloock, Vero Vanden Abeele, Johan A. K. Suykens, Luc Geurts:
The skweezee system: enabling the design and the programming of squeeze interactions. UIST 2013: 521-530 - 2012
- [c98]Carolina Varon, Dries Testelmans, Bertien Buyse, Johan A. K. Suykens, Sabine Van Huffel:
Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis. EMBC 2012: 3151-3154 - [c97]Vanya Van Belle, Sabine Van Huffel, Johan A. K. Suykens, Stephen P. Boyd:
Interval coded scoring systems for survival analysis. ESANN 2012 - [c96]Dries Geebelen, Kim Batselier, Philippe Dreesen, Marco Signoretto, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
Joint Regression and Linear Combination of Time Series for Optimal Prediction. ESANN 2012 - [c95]Carlos Alzate, Johan A. K. Suykens:
A semi-supervised formulation to binary kernel spectral clustering. IJCNN 2012: 1-8 - [c94]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Robustness of kernel based regression: Influence and weight functions. IJCNN 2012: 1-8 - [c93]Rocco Langone, Carlos Alzate, Johan A. K. Suykens:
Kernel spectral clustering for community detection in complex networks. IJCNN 2012: 1-8 - 2011
- [c92]Jorge López Lázaro, Kris De Brabanter, José R. Dorronsoro, Johan A. K. Suykens:
Sparse LS-SVMs with L0 - norm minimization. ESANN 2011 - [c91]Siamak Mehrkanoon, Li Jiang, Carlos Alzate, Johan A. K. Suykens:
Symbolic computing of LS-SVM based models. ESANN 2011 - [c90]Borbála Hunyadi, Maarten De Vos, Marco Signoretto, Johan A. K. Suykens, Wim Van Paesschen, Sabine Van Huffel:
Automatic Seizure Detection Incorporating Structural Information. ICANN (1) 2011: 233-240 - [c89]Rocco Langone, Carlos Alzate, Johan A. K. Suykens:
Modularity-based model selection for kernel spectral clustering. IJCNN 2011: 1849-1856 - [c88]Carlos Alzate, Johan A. K. Suykens:
Out-of-sample eigenvectors in kernel spectral clustering. IJCNN 2011: 2349-2356 - 2010
- [c87]Ion Necoara, Ioan Dumitrache, Johan A. K. Suykens:
Fast primal-dual projected linear iterations for distributed consensus in constrained convex optimization. CDC 2010: 1366-1371 - [c86]Tillmann Falck, Johan A. K. Suykens, Bart De Moor:
Linear parametric noise models for Least Squares Support Vector Machines. CDC 2010: 6389-6394 - [c85]Tillmann Falck, Johan A. K. Suykens, Johan Schoukens, Bart De Moor:
Nuclear norm regularization for overparametrized Hammerstein systems. CDC 2010: 7202-7207 - [c84]Marco Signoretto, Kristiaan Pelckmans, Lieven De Lathauwer, Johan A. K. Suykens:
Improved non-parametric sparse recovery with data matched penalties. CIP 2010: 46-51 - [c83]Carlos Alzate, Johan A. K. Suykens:
Highly sparse kernel spectral clustering with predictive out-of-sample extensions. ESANN 2010 - [c82]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
On the use of a clinical kernel in survival analysis. ESANN 2010 - [c81]Kristiaan Pelckmans, Toon van Waterschoot, Johan A. K. Suykens:
Efficient adaptive filtering for smooth linear FIR models. EUSIPCO 2010: 2136-2140 - [c80]Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens:
Kernel-Based Learning from Infinite Dimensional 2-Way Tensors. ICANN (2) 2010: 59-69 - [c79]Fabian Ojeda, Tillmann Falck, Bart De Moor, Johan A. K. Suykens:
Polynomial componentwise LS-SVM: Fast variable selection using low rank updates. IJCNN 2010: 1-7 - [c78]Fabian Ojeda, Marco Signoretto, Raf Van de Plas, Etienne Waelkens, Bart De Moor, Johan A. K. Suykens:
Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging. PRIB 2010: 325-334 - 2009
- [c77]Jan Luts, Johan A. K. Suykens, Sabine Van Huffel, Teresa Laudadio, Sofie Van Cauter, Uwe Himmelreich, Enrique Molla, Jose Piquer, M. Carmen Martínez-Bisbal, Bernardo Celda:
Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI. CBMS 2009: 1-8 - [c76]Ion Necoara, Carlo Savorgnan, Dinh Quoc Tran, Johan A. K. Suykens, Moritz Diehl:
Distributed nonlinear optimal control using sequential convex programming and smoothing techniques. CDC 2009: 543-548 - [c75]Tillmann Falck, Johan A. K. Suykens, Bart De Moor:
Robustness analysis for Least Squares kernel based regression: an optimization approach. CDC 2009: 6774-6779 - [c74]Kristiaan Pelckmans, Johan A. K. Suykens:
Transductively Learning from Positive Examples Only. ESANN 2009 - [c73]Ion Necoara, Johan A. K. Suykens:
A dual interior-point distributed algorithm for large-scale data networks optimization. ECC 2009: 969-974 - [c72]Paschalis Tsiaflakis, Ion Necoara, Johan A. K. Suykens, Marc Moonen:
An improved dual decomposition approach to DSL dynamic spectrum management. EUSIPCO 2009: 2087-2091 - [c71]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
MINLIP: Efficient Learning of Transformation Models. ICANN (1) 2009: 60-69 - [c70]Kris De Brabanter, Kristiaan Pelckmans, Jos De Brabanter, Michiel Debruyne, Johan A. K. Suykens, Mia Hubert, Bart De Moor:
Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes. ICANN (1) 2009: 100-110 - [c69]Carlos Alzate, Marcelo Espinoza, Bart De Moor, Johan A. K. Suykens:
Identifying Customer Profiles in Power Load Time Series Using Spectral Clustering. ICANN (2) 2009: 315-324 - [c68]Adrien Combaz, Nikolay V. Manyakov, Nikolay Chumerin, Johan A. K. Suykens, Marc M. Van Hulle:
Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling. ICMLA 2009: 386-391 - [c67]Carlos Alzate, Johan A. K. Suykens:
A regularized formulation for spectral clustering with pairwise constraints. IJCNN 2009: 141-148 - [c66]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints. IWANN (1) 2009: 65-72 - [c65]Nikolay Chumerin, Nikolay V. Manyakov, Adrien Combaz, Johan A. K. Suykens, Refet Firat Yazicioglu, Tom Torfs, Patrick Merken, Herc P. Neves, Chris Van Hoof, Marc M. Van Hulle:
P300 Detection Based on Feature Extraction in On-line Brain-Computer Interface. KI 2009: 339-346 - 2008
- [c64]Ion Necoara, Minh Dang Doan, Johan A. K. Suykens:
Application of the proximal center decomposition method to distributed model predictive control. CDC 2008: 2900-2905 - [c63]Ion Necoara, Johan A. K. Suykens:
A proximal center-based decomposition method for multi-agent convex optimization. CDC 2008: 3077-3082 - [c62]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
Survival SVM: a practical scalable algorithm. ESANN 2008: 89-94 - [c61]Marco Signoretto, Kristiaan Pelckmans, Johan A. K. Suykens:
Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation. ICANN (1) 2008: 175-184 - [c60]Carlos Alzate, Johan A. K. Suykens:
Sparse kernel models for spectral clustering using the incomplete Cholesky decomposition. IJCNN 2008: 3556-3563 - 2007
- [c59]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Convex optimization for the design of learning machines. ESANN 2007: 193-204 - [c58]Ben Van Calster, Jan Luts, Johan A. K. Suykens, George Condous, Tom Bourne, Dirk Timmerman, Sabine Van Huffel:
Comparing Methods for Multi-class Probabilities in Medical Decision Making Using LS-SVMs and Kernel Logistic Regression. ICANN (2) 2007: 139-148 - [c57]Joos Vandewalle, Johan A. K. Suykens, Bart De Moor, Amaury Lendasse:
State-of-the-Art and Evolution in Public Data Sets and Competitions for System Identification, Time Series Prediction and Pattern Recognition. ICASSP (4) 2007: 1269-1272 - [c56]Peter Karsmakers, Kristiaan Pelckmans, Johan A. K. Suykens:
Multi-class kernel logistic regression: a fixed-size implementation. IJCNN 2007: 1756-1761 - [c55]Fabian Ojeda, Johan A. K. Suykens, Bart De Moor:
Variable selection by rank-one updates for least squares support vector machines. IJCNN 2007: 2283-2288 - [c54]Carlos Alzate, Johan A. K. Suykens:
ICA through an LS-SVM based Kernel CCA Measure for Independence. IJCNN 2007: 2920-2925 - [c53]Peter Karsmakers, Kristiaan Pelckmans, Johan A. K. Suykens, Hugo Van hamme:
Fixed-size kernel logistic regression for phoneme classification. INTERSPEECH 2007: 78-81 - [c52]Kristiaan Pelckmans, Johan A. K. Suykens:
Transductive Rademacher Complexities for Learning Over a Graph. MLG 2007 - [c51]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
A Risk Minimization Principle for a Class of Parzen Estimators. NIPS 2007: 1137-1144 - [c50]Kristiaan Pelckmans, John Shawe-Taylor, Johan A. K. Suykens, Bart De Moor:
Margin based Transductive Graph Cuts using Linear Programming. AISTATS 2007: 363-370 - 2006
- [c49]Lars Imsland, J. Anthony Rossiter, Bert Pluymers, Johan A. K. Suykens:
Robust triple mode MPC. ACC 2006 - [c48]Bert Pluymers, Mayuresh V. Kothare, Johan A. K. Suykens, Bart De Moor:
Robust synthesis of constrained linear state feedback using LMIs and polyhedral invariant sets. ACC 2006 - [c47]Carlos Alzate, Johan A. K. Suykens:
A Weighted Kernel PCA Formulation with Out-of-Sample Extensions for Spectral Clustering Methods. IJCNN 2006: 138-144 - [c46]Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle:
Multi-scroll and hypercube attractors from Josephson junctions. ISCAS 2006 - 2005
- [c45]Bert Pluymers, John Anthony Rossiter, Johan A. K. Suykens, Bart De Moor:
The efficient computation of polyhedral invariant sets for linear systems with polytopic uncertainty. ACC 2005: 804-809vol.2 - [c44]Bert Pluymers, John Anthony Rossiter, Johan A. K. Suykens, Bart De Moor:
Interpolation based MPC for LPV systems using polyhedral invariant sets. ACC 2005: 810-815vol.2 - [c43]J. Anthony Rossiter, Yihang Ding, Bert Pluymers, Johan A. K. Suykens, Bart De Moor:
Interpolation based robust MPC with exact constraint handling. CDC/ECC 2005: 302-307 - [c42]Kristiaan Pelckmans, Johan A. K. Suykens, Ivan Goethals, Bart De Moor:
On Model Complexity Control in Identification of Hammerstein Systems. CDC/ECC 2005: 1203-1208 - [c41]Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor:
Imposing Symmetry in Least Squares Support Vector Machines Regression. CDC/ECC 2005: 5716-5721 - [c40]Ivan Goethals, Kristiaan Pelckmans, Luc Hoegaerts, Johan A. K. Suykens, Bart De Moor:
Subspace intersection identification of Hammerstein-Wiener systems. CDC/ECC 2005: 7108-7113 - [c39]Nathalie Pochet, Frizo A. L. Janssens, Frank De Smet, Kathleen Marchal, Ignace Vergote, Johan A. K. Suykens, Bart De Moor:
M@CBETH: Optimizing Clinical Microarray Classification. CSB Workshops 2005: 89-90 - [c38]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Componentwise Support Vector Machines for Structure Detection. ICANN (2) 2005: 643-648 - [c37]Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle:
Spatiotemporal pattern formation in the ACE16k CNN chip. ISCAS (6) 2005: 5814-5817 - [c36]Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor:
Load Forecasting Using Fixed-Size Least Squares Support Vector Machines. IWANN 2005: 1018-1026 - 2004
- [c35]Bert Pluymers, Johan A. K. Suykens, Bart De Moor:
Linear MPC with time-varying terminal cost using sparse convex combinations and bisection search. CDC 2004: 2029-2034 - [c34]Bert Pluymers, Johan A. K. Suykens, Bart De Moor:
Robust finite-horizon MPC using optimal worst-case closed-loop predictions. CDC 2004: 2503-2508 - [c33]Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor:
Partially linear models and least squares support vector machines. CDC 2004: 3388-3393 - [c32]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Sparse LS-SVMs using additive regularization with a penalized validation criterion. ESANN 2004: 435-440 - [c31]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs. ICONIP 2004: 1216-1222 - [c30]Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
A Comparison of Pruning Algorithms for Sparse Least Squares Support Vector Machines. ICONIP 2004: 1247-1253 - [c29]Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle:
A double scroll based true random bit generator. ISCAS (4) 2004: 581-584 - [c28]Tijl De Bie, Johan A. K. Suykens, Bart De Moor:
Learning from General Label Constraints. SSPR/SPR 2004: 671-679 - 2003
- [c27]Chuan Lu, Tony Van Gestel, Johan A. K. Suykens, Sabine Van Huffel, Dirk Timmerman, Ignace Vergote:
Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines. AIME 2003: 219-228 - [c26]Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor:
Least squares support vector machines and primal space estimation. CDC 2003: 3451-3456 - [c25]Tony Van Gestel, Bart Baesens, Johan A. K. Suykens, Marcelo Espinoza, Dirk-Emma Baestaens, Jan Vanthienen, Bart De Moor:
Bankruptcy prediction with least squares support vector machine classifiers. CIFEr 2003: 1-8 - [c24]Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Kernel PLS variants for regression. ESANN 2003: 200-208 - [c23]Johan A. K. Suykens, Müstak E. Yalçin, Joos Vandewalle:
Coupled chaotic simulated annealing processes. ISCAS (3) 2003: 582-585 - [c22]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Variogram based noise variance estimation and its use in kernel based regression. NNSP 2003: 199-208 - 2002
- [c21]Lukas Lukas, Andy Devos, Johan A. K. Suykens, Leentje Vanhamme, Sabine Van Huffel, Anne Rosemary Tate, Carles Majós, Carles Arús:
The use of LS-SVM in the classification of brain tumors based on Magnetic Resonance Spectroscopy signals. ESANN 2002: 131-136 - [c20]Lieveke Ameye, Chuan Lu, Lukas Lukas, Jos De Brabanter, Johan A. K. Suykens, Sabine Van Huffel, Hans Daniels, Gunnar Naulaers, Hugo Devlieger:
Prediction of mental development of preterm newborns at birth time using LS-SVM. ESANN 2002: 167-172 - [c19]Jos De Brabanter, Kristiaan Pelckmans, Johan A. K. Suykens, Joos Vandewalle:
Robust Cross-Validation Score Function for Non-linear Function Estimation. ICANN 2002: 713-719 - [c18]Bart Hamers, Johan A. K. Suykens, Bart De Moor:
Compactly Supported RBF Kernels for Sparsifying the Gram Matrix in LS-SVM Regression Models. ICANN 2002: 720-726 - 2001
- [c17]Tony Van Gestel, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
Automatic relevance determination for Least Squares Support Vector Machines classifiers. ESANN 2001: 13-18 - [c16]Tony Van Gestel, Johan A. K. Suykens, Jos De Brabanter, Bart De Moor, Joos Vandewalle:
Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines. ICANN 2001: 384-389 - 2000
- [c15]Tony Van Gestel, Johan A. K. Suykens, Paul Van Dooren, Bart De Moor:
Imposing stability in subspace identification by regularization. CDC 2000: 1555-1560 - [c14]Johan A. K. Suykens, Lukas Lukas, Joos Vandewalle:
Sparse least squares Support Vector Machine classifiers. ESANN 2000: 37-42 - [c13]Johan A. K. Suykens, Joos Vandewalle:
The K.U.Leuven competition data: a challenge for advanced neural network techniques. ESANN 2000: 299-304 - [c12]Johan A. K. Suykens, Lukas Lukas, Joos Vandewalle:
Sparse approximation using least squares support vector machines. ISCAS 2000: 757-760 - [c11]Bart Baesens, Stijn Viaene, Tony Van Gestel, Johan A. K. Suykens, Guido Dedene, Bart De Moor, Jan Vanthienen:
An empirical assessment of kernel type performance for least squares support vector machine classifiers. KES 2000: 313-316 - [c10]Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene:
Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case. PKDD 2000: 657-664 - 1999
- [c9]Johan A. K. Suykens, Joos Vandewalle:
Multiclass least squares support vector machines. IJCNN 1999: 900-903 - [c8]Johan A. K. Suykens, Joos Vandewalle:
Continuous time NLq theory: absolute stability criteria. IJCNN 1999: 1481-1484 - [c7]Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle:
On the realization of n-scroll attractors. ISCAS (5) 1999: 483-486 - 1998
- [c6]Johan A. K. Suykens, Joos Vandewalle:
Improved generalization ability of neurocontrollers by imposing NLq stability constraints. ESANN 1998: 99-104 - 1997
- [c5]Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
Robust NLq neural control theory. ICNN 1997: 2396-2401 - 1995
- [c4]Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
NLq theory: unifications in the theory of neural networks, systems and control. ESANN 1995 - [c3]Johan A. K. Suykens, Joos Vandewalle:
Global asymptotic stability criteria for multilayer recurrent neural networks with applications to modelling and control. ICNN 1995: 1065-1069 - [c2]Johan A. K. Suykens, Joos Vandewalle:
On the identification of a chaotic system by means of recurrent neural state space models. ICNN 1995: 1570-1573 - [c1]Johan A. K. Suykens, Joos Vandewalle:
Generalized Cellular Neural Networks Represented in he NLq Framework. ISCAS 1995: 645-648
Parts in Books or Collections
- 2015
- [p2]Raghvendra Mall, Johan A. K. Suykens:
KSC-net: Community Detection for Big Data Networks. Big Data - Algorithms, Analytics, and Applications 2015: 157-174 - [p1]Marco Signoretto, Johan A. K. Suykens:
Kernel Methods. Handbook of Computational Intelligence 2015: 577-605
Informal and Other Publications
- 2024
- [i85]Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A. K. Suykens:
Nonlinear functional regression by functional deep neural network with kernel embedding. CoRR abs/2401.02890 (2024) - [i84]Zhongjie Shi, Fanghui Liu, Yuan Cao, Johan A. K. Suykens:
Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint. CoRR abs/2401.13624 (2024) - [i83]Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens:
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes. CoRR abs/2402.01476 (2024) - [i82]Qinghua Tao, Xiangming Xi, Jun Xu, Johan A. K. Suykens:
Sparsity via Sparse Group k-max Regularization. CoRR abs/2402.08493 (2024) - [i81]Bram De Cooman, Johan A. K. Suykens:
A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints. CoRR abs/2404.16468 (2024) - [i80]Joris Depoortere, Johan Driesen, Johan A. K. Suykens, Hussain Syed Kazmi:
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe. CoRR abs/2405.14472 (2024) - [i79]Sonny Achten, Francesco Tonin, Volkan Cevher, Johan A. K. Suykens:
HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity. CoRR abs/2405.17050 (2024) - [i78]Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens:
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning. CoRR abs/2406.01435 (2024) - [i77]Qinghua Tao, Francesco Tonin, Alex Lambert, Yingyi Chen, Panagiotis Patrinos, Johan A. K. Suykens:
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method. CoRR abs/2406.08748 (2024) - 2023
- [i76]Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens:
Multi-view Kernel PCA for Time series Forecasting. CoRR abs/2301.09811 (2023) - [i75]Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Semi-Supervised Classification with Graph Convolutional Kernel Machines. CoRR abs/2301.13764 (2023) - [i74]Francesco Tonin, Qinghua Tao, Panagiotis Patrinos, Johan A. K. Suykens:
Deep Kernel Principal Component Analysis for Multi-level Feature Learning. CoRR abs/2302.11220 (2023) - [i73]Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens:
Tensorized LSSVMs for Multitask Regression. CoRR abs/2303.02451 (2023) - [i72]Konstantinos Kontras, Christos Chatzichristos, Huy Phan, Johan A. K. Suykens, Maarten De Vos:
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities. CoRR abs/2304.06485 (2023) - [i71]Sonny Achten, Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens:
Duality in Multi-View Restricted Kernel Machines. CoRR abs/2305.17251 (2023) - [i70]Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens:
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. CoRR abs/2305.19798 (2023) - [i69]Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens:
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. CoRR abs/2306.05815 (2023) - [i68]Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers. CoRR abs/2306.07015 (2023) - [i67]Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method. CoRR abs/2306.07040 (2023) - [i66]Joran Michiels, Maarten De Vos, Johan A. K. Suykens:
Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value. CoRR abs/2306.10880 (2023) - [i65]Joran Michiels, Maarten De Vos, Johan A. K. Suykens:
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions. CoRR abs/2306.16431 (2023) - [i64]Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool:
Unbalanced Optimal Transport: A Unified Framework for Object Detection. CoRR abs/2307.02402 (2023) - [i63]Henri De Plaen, Johan A. K. Suykens:
A Dual Formulation for Probabilistic Principal Component Analysis. CoRR abs/2307.10078 (2023) - [i62]Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Xiaolin Huang, Johan A. K. Suykens:
Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs. CoRR abs/2308.16056 (2023) - [i61]Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens:
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels. CoRR abs/2310.05236 (2023) - [i60]Mihaly Novak, Rocco Langone, Carlos Alzate, Johan A. K. Suykens:
Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems. CoRR abs/2310.13381 (2023) - 2022
- [i59]Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens:
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation. CoRR abs/2202.01397 (2022) - [i58]Qinghua Tao, Li Li, Xiaolin Huang, Xiangming Xi, Shuning Wang, Johan A. K. Suykens:
Piecewise Linear Neural Networks and Deep Learning. CoRR abs/2206.09149 (2022) - [i57]Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens:
Compressing Features for Learning with Noisy Labels. CoRR abs/2206.13140 (2022) - [i56]Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Tensor-based Multi-view Spectral Clustering via Shared Latent Space. CoRR abs/2207.11559 (2022) - [i55]Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens:
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer. CoRR abs/2207.11971 (2022) - 2021
- [i54]Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. CoRR abs/2102.08443 (2021) - [i53]Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. CoRR abs/2104.02373 (2021) - [i52]Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens:
Boosting Co-teaching with Compression Regularization for Label Noise. CoRR abs/2104.13766 (2021) - [i51]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. CoRR abs/2105.13942 (2021) - [i50]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. CoRR abs/2105.13949 (2021) - [i49]Fanghui Liu, Johan A. K. Suykens, Volkan Cevher:
On the Double Descent of Random Features Models Trained with SGD. CoRR abs/2110.06910 (2021) - [i48]Maximilian Lucassen, Johan A. K. Suykens, Kim Batselier:
Tensor Network Kalman Filtering for Large-Scale LS-SVMs. CoRR abs/2110.13501 (2021) - 2020
- [i47]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality. CoRR abs/2002.01180 (2020) - [i46]Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens:
Wasserstein Exponential Kernels. CoRR abs/2002.01878 (2020) - [i45]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Diversity sampling is an implicit regularization for kernel methods. CoRR abs/2002.08616 (2020) - [i44]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond. CoRR abs/2004.11154 (2020) - [i43]Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Generalizing Random Fourier Features via Generalized Measures. CoRR abs/2006.00247 (2020) - [i42]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. CoRR abs/2006.01073 (2020) - [i41]Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Disentangled Representation Learning and Generation with Manifold Optimization. CoRR abs/2006.07046 (2020) - [i40]Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks. CoRR abs/2006.09096 (2020) - [i39]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. CoRR abs/2006.13701 (2020) - [i38]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. CoRR abs/2006.14331 (2020) - [i37]Joachim Schreurs, Iwein Vranckx, Bart De Ketelaere, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw:
Outlier detection in non-elliptical data by kernel MRCD. CoRR abs/2008.02046 (2020) - [i36]Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimension: Refined analysis beyond double descent. CoRR abs/2010.02681 (2020) - [i35]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. CoRR abs/2011.01668 (2020) - [i34]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. CoRR abs/2011.06964 (2020) - [i33]Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. CoRR abs/2011.12659 (2020) - 2019
- [i32]Hanyuan Hang, Yingyi Chen, Johan A. K. Suykens:
Two-stage Best-scored Random Forest for Large-scale Regression. CoRR abs/1905.03438 (2019) - [i31]Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A. K. Suykens, Shuning Wang:
Efficient hinging hyperplanes neural network and its application in nonlinear system identification. CoRR abs/1905.06518 (2019) - [i30]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Nyström landmark sampling and regularized Christoffel functions. CoRR abs/1905.12346 (2019) - [i29]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Generative Restricted Kernel Machines. CoRR abs/1906.08144 (2019) - [i28]Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens:
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. CoRR abs/1911.09158 (2019) - 2018
- [i27]Hussain Syed Kazmi, Johan A. K. Suykens, Johan Driesen:
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings. CoRR abs/1803.03491 (2018) - [i26]Anna Marconato, Jonas Sjöberg, Johan A. K. Suykens, Johan Schoukens:
Improved Initialization for Nonlinear State-Space Modeling. CoRR abs/1804.08654 (2018) - [i25]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions. CoRR abs/1809.09910 (2018) - [i24]Zahra Karevan, Johan A. K. Suykens:
Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting. CoRR abs/1811.06341 (2018) - 2017
- [i23]Carlos M. Alaíz, Johan A. K. Suykens:
Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers. CoRR abs/1706.05928 (2017) - [i22]Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens:
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions. CoRR abs/1711.07271 (2017) - 2016
- [i21]Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens:
Learning theory estimates with observations from general stationary stochastic processes. CoRR abs/1605.02887 (2016) - [i20]Michaël Fanuel, Carlos M. Alaíz, Johan A. K. Suykens:
Magnetic eigenmaps for community detection in directed networks. CoRR abs/1606.07359 (2016) - [i19]Michaël Fanuel, Ángela Fernández, Carlos M. Alaíz, Johan A. K. Suykens:
Magnetic Eigenmaps for Visualization of Directed Networks. CoRR abs/1606.08266 (2016) - [i18]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning. CoRR abs/1610.06811 (2016) - [i17]Zhongming Chen, Kim Batselier, Johan A. K. Suykens, Ngai Wong:
Parallelized Tensor Train Learning of Polynomial Classifiers. CoRR abs/1612.06505 (2016) - [i16]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Robust Classification of Graph-Based Data. CoRR abs/1612.07141 (2016) - 2015
- [i15]Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens:
A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification. CoRR abs/1502.01563 (2015) - [i14]Yuning Yang, Siamak Mehrkanoon, Johan A. K. Suykens:
Higher order Matching Pursuit for Low Rank Tensor Learning. CoRR abs/1503.02216 (2015) - [i13]Rocco Langone, Raghvendra Mall, Carlos Alzate, Johan A. K. Suykens:
Kernel Spectral Clustering and applications. CoRR abs/1505.00477 (2015) - [i12]Xiaolin Huang, Lei Shi, Ming Yan, Johan A. K. Suykens:
Pinball Loss Minimization for One-bit Compressive Sensing. CoRR abs/1505.03898 (2015) - [i11]Emanuele Frandi, Ricardo Ñanculef, Stefano Lodi, Claudio Sartori, Johan A. K. Suykens:
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee. CoRR abs/1510.07169 (2015) - 2014
- [i10]Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor:
A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models. CoRR abs/1402.3144 (2014) - [i9]Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor:
Fast Prediction with SVM Models Containing RBF Kernels. CoRR abs/1403.0736 (2014) - [i8]Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor:
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines. CoRR abs/1403.0745 (2014) - [i7]Andreas Argyriou, Marco Signoretto, Johan A. K. Suykens:
Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization. CoRR abs/1404.3591 (2014) - [i6]Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens:
Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning. CoRR abs/1410.4062 (2014) - [i5]Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks. CoRR abs/1411.7640 (2014) - 2013
- [i4]Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens:
Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties. CoRR abs/1310.4977 (2013) - 2007
- [i3]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Support and Quantile Tubes. CoRR abs/cs/0703055 (2007) - 2005
- [i2]Kristiaan Pelckmans, Ivan Goethals, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Componentwise Least Squares Support Vector Machines. CoRR abs/cs/0504086 (2005) - 2002
- [i1]Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Intelligence and Cooperative Search by Coupled Local Minimizers. CoRR cs.AI/0210030 (2002)
Coauthor Index
[b2] [b1] [j192] [j189] [j123] [j112] [j108] [j105] [j87] [j86] [j79] [j75] [j71] [j70] [j69] [j68] [j63] [j60] [j54] [j52] [j51] [j46] [j45] [j44] [j43] [j42] [j41] [j40] [j39] [j38] [j37] [j36] [j35] [j33] [j32] [j27] [j26] [j23] [j22] [j19] [j18] [j17] [j16] [j15] [j14] [j12] [j9] [j7] [j5] [j4] [j2] [j1] [c183] [c174] [c173] [c127] [c120] [c96] [c94] [c86] [c85] [c79] [c78] [c75] [c70] [c69] [c59] [c57] [c55] [c51] [c50] [c48] [c45] [c44] [c43] [c42] [c41] [c40] [c39] [c38] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c28] [c26] [c25] [c24] [c22] [c18] [c17] [c16] [c15] [c11] [c10] [c5] [c4] [i76] [i71] [i53] [i40] [i10] [i9] [i8] [i3] [i2] [i1]
[b2] [b1] [j106] [j84] [j65] [j55] [j54] [j47] [j42] [j32] [j31] [j30] [j26] [j25] [j24] [j23] [j22] [j20] [j19] [j17] [j16] [j14] [j13] [j12] [j11] [j10] [j9] [j8] [j7] [j6] [j5] [j4] [j3] [j1] [c96] [c94] [c57] [c46] [c37] [c30] [c29] [c24] [c23] [c19] [c17] [c16] [c14] [c13] [c12] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [c2] [c1] [i1]
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