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2010 – 2019
- 2015
[p1]Marco Signoretto, Johan A. K. Suykens:
Kernel Methods. Handbook of Computational Intelligence 2015: 577-605- 2014
- [j5]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) - [c9]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 - [i2]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) - 2013
- [j4]Jose Manuel Gil-Cacho, Marco Signoretto, Toon van Waterschoot, Marc Moonen, Søren Holdt Jensen:
Nonlinear Acoustic Echo Cancellation Based on a Sliding-Window Leaky Kernel Affine Projection Algorithm. IEEE Trans. Speech Audio Process. 21(9): 1867-1878 (2013) - [c8]Marco Signoretto, Johan A. K. Suykens:
DynOpt: Incorporating dynamics into mean-variance portfolio optimization. CIFEr 2013: 48-54 - [c7]Borbála Hunyadi, Marco Signoretto, Stefan Debener, Sabine Van Huffel, Maarten De Vos:
Classification of Structured EEG Tensors Using Nuclear Norm Regularization: Improving P300 Classification. PRNI 2013: 98-101 - [i1]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) - 2012
- [j3]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) - [c6]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 - 2011
- [b1]Marco Signoretto:
Kernels and Tensors for Structured Data Modelling (Kernels en tensoren voor het modelleren van gestructureerde data). Katholieke Universiteit Leuven, Belgium, 2011 - [j2]Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens:
A kernel-based framework to tensorial data analysis. Neural Networks 24(8): 861-874 (2011) - [j1]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) - [c5]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 - 2010
- [c4]Marco Signoretto, Kristiaan Pelckmans, Lieven De Lathauwer, Johan A. K. Suykens:
Improved non-parametric sparse recovery with data matched penalties. CIP 2010: 46-51 - [c3]Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens:
Kernel-Based Learning from Infinite Dimensional 2-Way Tensors. ICANN (2) 2010: 59-69 - [c2]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
2000 – 2009
- 2008
- [c1]Marco Signoretto, Kristiaan Pelckmans, Johan A. K. Suykens:
Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation. ICANN (1) 2008: 175-184
Coauthor Index
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