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ORCIDVladimir Cherkassky
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2020 – today
- 2024
[j48]Vladimir Cherkassky, Eng Hock Lee
:
To understand double descent, we need to understand VC theory. Neural Networks 169: 242-256 (2024)- [i6]Vladimir Cherkassky, Eng Hock Lee:
A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition. CoRR abs/2408.06598 (2024) - 2022
- [i5]Eng Hock Lee, Vladimir Cherkassky:
VC Theoretical Explanation of Double Descent. CoRR abs/2205.15549 (2022) - 2020
- [j47]Hsiang-Han Chen, Vladimir Cherkassky:
Performance metrics for online seizure prediction. Neural Networks 128: 22-32 (2020)
2010 – 2019
- 2019
- [c53]Vladimir Cherkassky, Hsiang-Han Chen, Han-Tai Shiao:
Group Learning for High-Dimensional Sparse Data. IJCNN 2019: 1-10 - [c52]Sauptik Dhar, Vladimir Cherkassky, Mohak Shah:
Multiclass Learning from Contradictions. NeurIPS 2019: 8398-8408 - [i4]Sauptik Dhar, Vladimir Cherkassky:
Single Class Universum-SVM. CoRR abs/1909.09862 (2019) - 2018
- [j46]Patoomsiri Songsiri, Vladimir Cherkassky, Boonserm Kijsirikul:
Universum Selection for Boosting the Performance of Multiclass Support Vector Machines Based on One-versus-One Strategy. Knowl. Based Syst. 159: 9-19 (2018) - [i3]Sauptik Dhar, Vladimir Cherkassky, Mohak Shah:
Multiclass Universum SVM. CoRR abs/1808.08111 (2018) - 2017
- [j45]Ying Yang, Jing Wang, Cyntia Bailer, Vladimir Cherkassky, Marcel Adam Just:
Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function. NeuroImage 146: 658-666 (2017) - [j44]Marcel Adam Just, Jing Wang, Vladimir Cherkassky:
Neural representations of the concepts in simple sentences: Concept activation prediction and context effects. NeuroImage 157: 511-520 (2017) - [j43]Han-Tai Shiao, Vladimir Cherkassky, Jieun Lee, Brandon Veber, Edward E. Patterson, Benjamin H. Brinkmann, Gregory A. Worrell:
SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal. IEEE Trans. Biomed. Eng. 64(5): 1011-1022 (2017) - [c51]Sauptik Dhar, Vladimir Cherkassky:
Universum learning for SVM regression. IJCNN 2017: 3641-3648 - 2016
- [i2]Sauptik Dhar, Vladimir Cherkassky:
Universum Learning for SVM Regression. CoRR abs/1605.08497 (2016) - [i1]Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah:
Universum Learning for Multiclass SVM. CoRR abs/1609.09162 (2016) - 2015
- [j42]Sauptik Dhar, Vladimir Cherkassky:
Development and Evaluation of Cost-Sensitive Universum-SVM. IEEE Trans. Cybern. 45(4): 806-818 (2015) - [c50]Adarsh Sivasankaran, Vladimir Cherkassky, Mark Albrecht, Eric Williams, Martin Maiers:
Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM. ICMLA 2015: 831-836 - [c49]Vladimir Cherkassky, Brandon Veber, Jieun Lee, Han-Tai Shiao, Ned Patterson, Gregory A. Worrell, Benjamin H. Brinkmann:
Reliable seizure prediction from EEG data. IJCNN 2015: 1-8 - 2014
- [c48]Han-Tai Shiao, Vladimir Cherkassky:
Learning using privileged information (LUPI) for modeling survival data. IJCNN 2014: 1042-1049 - 2012
- [j41]Feng Cai, Vladimir Cherkassky:
Generalized SMO Algorithm for SVM-Based Multitask Learning. IEEE Trans. Neural Networks Learn. Syst. 23(6): 997-1003 (2012) - [c47]Sauptik Dhar, Vladimir Cherkassky:
Cost-Sensitive Universum-SVM. ICMLA (1) 2012: 220-225 - [c46]Han-Tai Shiao, Vladimir Cherkassky:
Implementation and comparison of SVM-based Multi-Task Learning methods. IJCNN 2012: 1-7 - [c45]Vladimir Cherkassky:
Predictive Learning, Knowledge Discovery and Philosophy of Science. WCCI 2012: 209-233 - 2011
- [j40]Vladimir Cherkassky, Sauptik Dhar, Wuyang Dai:
Practical Conditions for Effectiveness of the Universum Learning. IEEE Trans. Neural Networks 22(8): 1241-1255 (2011) - [c44]Sauptik Dhar, Vladimir Cherkassky:
Application of SOM to analysis of Minnesota soil survey data. IJCNN 2011: 633-639 - [c43]Vladimir Cherkassky, Sohini Roy Chowdhury, Volker Landenberger, Saurabh Tewari, Paul Bursch:
Prediction of electric power consumption for commercial buildings. IJCNN 2011: 666-672 - 2010
- [c42]Vladimir Cherkassky, Sauptik Dhar:
Simple Method for Interpretation of High-Dimensional Nonlinear SVM Classification Models. DMIN 2010: 267-272
2000 – 2009
- 2009
- [j39]Lichen Liang, Feng Cai, Vladimir Cherkassky:
Predictive learning with structured (grouped) data. Neural Networks 22(5-6): 766-773 (2009) - [j38]Vladimir Cherkassky, Yunqian Ma:
Another look at statistical learning theory and regularization. Neural Networks 22(7): 958-969 (2009) - [c41]Kai-min Kevin Chang, Vladimir Cherkassky, Tom M. Mitchell, Marcel Adam Just:
Quantitative modeling of the neural representation of adjective-noun phrases to account for fMRI activation. ACL/IJCNLP 2009: 638-646 - [c40]Vladimir Cherkassky, Wuyang Dai:
Empirical Study of the Universum SVM Learning for High-Dimensional Data. ICANN (1) 2009: 932-941 - [c39]Feng Cai, Vladimir Cherkassky:
SVM+ regression and multi-task learning. IJCNN 2009: 418-424 - [c38]Vladimir Cherkassky, Feng Cai, Lichen Liang:
Predictive learning with sparse heterogeneous data. IJCNN 2009: 544-551 - 2008
- [c37]Xue Bai, Vladimir Cherkassky:
Gender classification of human faces using inference through contradictions. IJCNN 2008: 746-750 - [c36]Lichen Liang, Vladimir Cherkassky:
Connection between SVM+ and multi-task learning. IJCNN 2008: 2048-2054 - 2007
- [j37]Vladimir Cherkassky, William Hsieh, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés:
Computational intelligence in earth and environmental sciences. Neural Networks 20(4): 433 (2007) - [c35]Lichen Liang, Vladimir Cherkassky:
Learning Using Structured Data: Application to fMRI Data Analysis. IJCNN 2007: 495-499 - [c34]Tao Xiong, Jinbo Bi, R. Bharat Rao, Vladimir Cherkassky:
Probabilistic Joint Feature Selection for Multi-task Learning. SDM 2007: 332-342 - 2006
- [j36]Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés:
2006 Special issue: Earth Sciences and Environmental Applications of Computational IntelligenceIntroduction. Neural Networks 19(2): 111 (2006) - [j35]Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés:
Computational intelligence in earth sciences and environmental applications: Issues and challenges. Neural Networks 19(2): 113-121 (2006) - [c33]Jieping Ye, Tao Xiong, Qi Li, Ravi Janardan, Jinbo Bi, Vladimir Cherkassky, Chandra Kambhamettu:
Efficient model selection for regularized linear discriminant analysis. CIKM 2006: 532-539 - [c32]Tao Xiong, Jieping Ye, Vladimir Cherkassky:
Kernel Uncorrelated and Orthogonal Discriminant Analysis: A Unified Approach. CVPR (1) 2006: 125-131 - [c31]Lichen Liang, Vladimir Cherkassky, David A. Rottenberg:
Spatial SVM for feature selection and fMRI activation detection. IJCNN 2006: 1463-1469 - [c30]Hui Gao, Vladimir Cherkassky:
Real-Time Pricing of Mutual Funds. IJCNN 2006: 2402-2408 - 2005
- [j34]Hideya Koshino, Patricia A. Carpenter, Nancy J. Minshew, Vladimir Cherkassky, Timothy A. Keller, Marcel Adam Just:
Functional connectivity in an fMRI working memory task in high-functioning autism. NeuroImage 24(3): 810-821 (2005) - [j33]Stephen LaConte, Stephen C. Strother, Vladimir Cherkassky, Jon R. Anderson, Xiaoping Hu:
Support vector machines for temporal classification of block design fMRI data. NeuroImage 26(2): 317-329 (2005) - [j32]Vladimir Cherkassky, Yunqian Ma:
Multiple model regression estimation. IEEE Trans. Neural Networks 16(4): 785-798 (2005) - [c29]Vladimir Cherkassky, Yunqian Ma:
Support vector machines and regularization. SIP 2005: 166-171 - 2004
- [j31]Vladimir Cherkassky, Yunqian Ma:
Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17(1): 113-126 (2004) - [j30]Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Cherkassky:
Motion Estimation Using Statistical Learning Theory. IEEE Trans. Pattern Anal. Mach. Intell. 26(4): 466-478 (2004) - [c28]Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladimir Cherkassky:
Efficient Kernel Discriminant Analysis via QR Decomposition. NIPS 2004: 1529-1536 - 2003
- [j29]Vladimir Cherkassky, Yunqian Ma:
Comparison of Model Selection for Regression. Neural Comput. 15(7): 1691-1714 (2003) - [c27]Zoran Duric, Fayin Li, Harry Wechsler, Vladimir Cherkassky:
Controlling Model Complexity in Flow Estimation. ICCV 2003: 908-914 - 2002
- [j28]Vladimir Cherkassky:
Model complexity control and statistical learning theory. Nat. Comput. 1(1): 109-133 (2002) - [c26]Vladimir Cherkassky, Yunqian Ma:
Selection of Meta-parameters for Support Vector Regression. ICANN 2002: 687-693 - [c25]Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Cherkassky:
Motion Prediction Using VC-Generalization Bounds. ICPR (1) 2002: 151-154 - 2001
- [j27]Vladimir Cherkassky, Xuhui Shao:
Signal estimation and denoising using VC-theory. Neural Networks 14(1): 37-52 (2001) - [j26]Vladimir Cherkassky, Steven Kilts:
Myopotential denoising of ECG signals using wavelet thresholding methods. Neural Networks 14(8): 1129-1137 (2001) - [c24]Vladimir Cherkassky, Steven Kilts:
Comparison of Wavelet Thresholding Methods for Denoising ECG Signals. ICANN 2001: 625-629 - 2000
- [j25]Xuhui Shao, Vladimir Cherkassky, William Li:
Measuring the VC-Dimension Using Optimized Experimental Design. Neural Comput. 12(8): 1969-1986 (2000) - [j24]Rahul Singh, Vladimir Cherkassky, Nikolaos Papanikolopoulos:
Self-organizing maps for the skeletonization of sparse shapes. IEEE Trans. Neural Networks Learn. Syst. 11(1): 241-248 (2000) - [c23]Vladimir Cherkassky:
Introduction to VC learning theory with applications to Financial Engineering - Tutorial. CIFEr 2000: 1-2 - [c22]Vladimir Cherkassky, Filip Mulier, Anna B. Sheng:
Funds exchange: an approach for risk and portfolio management. CIFEr 2000: 3-7 - [c21]Shi Zhong, Vladimir Cherkassky:
Image Denoising Using Wavelet Thresholding and Model Selection. ICIP 2000: 262-265
1990 – 1999
- 1999
- [j23]Vladimir Cherkassky, Xuhui Shao, Filip Mulier, Vladimir Vapnik:
Model complexity control for regression using VC generalization bounds. IEEE Trans. Neural Networks 10(5): 1075-1089 (1999) - [c20]Shi Zhong, Vladimir Cherkassky:
Factors controlling generalization ability of MLP networks. IJCNN 1999: 625-630 - [c19]Xuhui Shao, Vladimir Cherkassky:
Multi-resolution support vector machine. IJCNN 1999: 1065-1070 - 1998
- [c18]Rahul Singh, Nikolaos Papanikolopoulos, Vladimir Cherkassky:
Object Skeletons from Sparse Shapes in Industrial image Settings. ICRA 1998: 3388-3393 - 1997
- [j22]Vladimir Cherkassky:
The Nature Of Statistical Learning Theory. IEEE Trans. Neural Networks 8(6): 1564 (1997) - [c17]Rahul Singh, Vladimir Cherkassky, Nikolaos P. Papanikolopoulos:
Determining the skeletal description of sparse shapes. CIRA 1997: 368-373 - [c16]Vladimir Cherkassky, Y. Kim, Filip Mulier:
Constrained Topological Maps for Regression and Classification. ICONIP (1) 1997: 330-333 - 1996
- [j21]Vladimir Cherkassky, Don Gehring, Filip Mulier:
Comparison of adaptive methods for function estimation from samples. IEEE Trans. Neural Networks 7(4): 969-984 (1996) - 1995
- [j20]Reza Rooholamini, Vladimir Cherkassky:
ATM-Based Multimedia Servers. IEEE Multim. 2(1): 39-52 (1995) - [j19]Filip Mulier, Vladimir Cherkassky:
Self-organization as an iterative kernel smoothing process. Neural Comput. 7(6): 1165-1177 (1995) - [j18]Filip Mulier, Vladimir Cherkassky:
Statistical analysis of self-organization. Neural Networks 8(5): 717-727 (1995) - 1994
- [j17]Reza Rooholamini, Vladimir Cherkassky, Mark Garver:
Finding the Right ATM Switch for the Market. Computer 27(4): 16-28 (1994) - [j16]Young-Keun Park, Vladimir Cherkassky:
Neural Network for Control of Rearrangeable Clos Networks. Int. J. Neural Syst. 5(3): 195-205 (1994) - [j15]Young-Keun Park, Vladimir Cherkassky, Gyungho Lee:
Omega network-based ATM switch with neural network-controlled bypass queueing and multiplexing. IEEE J. Sel. Areas Commun. 12(9): 1471-1480 (1994) - [j14]Kalavai J. Raghunath, Vladimir Cherkassky:
Noise Performance of Linear Associative Memories. IEEE Trans. Pattern Anal. Mach. Intell. 16(7): 757-765 (1994) - [c15]Filip Mulier, Vladimir Cherkassky:
Learning rate schedules for self-organizing maps. ICPR (2) 1994: 224-228 - [c14]Reza Rooholamini, Vladimir Cherkassky:
Moving ATM Closer to Multimedia Applications. LCN 1994: 278-287 - 1993
- [c13]Young-Keun Park, Vladimir Cherkassky:
Neural network controller for rearrangeable switching networks. ICNN 1993: 1896-1901 - [c12]D. Adkins, Vladimir Cherkassky, E. S. Olson:
Color Mapping Using Neural Networks. CIC 1993: 45-48 - [c11]Hossein Lari-Najafi, Vladimir Cherkassky:
Adaptive knot Placement for Nonparametric Regression. NIPS 1993: 247-254 - 1992
- [j13]Ee-Peng Lim, Vladimir Cherkassky:
Semantic Networks and Associative Databases: Two Approaches to Knowledge Representation and Reasoning. IEEE Expert 7(4): 31-40 (1992) - [j12]Vladimir Cherkassky, Hossein Lari-Najafi:
Data Representation for Diagnostic Neural Networks. IEEE Expert 7(5): 43-53 (1992) - 1991
- [j11]Vladimir Cherkassky, Malathi Rao, Harry Wechsler:
Fault-tolerant database using distributed associative memories. Inf. Sci. 53(1-2): 135-158 (1991) - [j10]Vladimir Cherkassky, Hossein Lari-Najafi:
Constrained topological mapping for nonparametric regression analysis. Neural Networks 4(1): 27-40 (1991) - [j9]Vladimir Cherkassky, Karen Fassett, Nikolaos Vassilas:
Linear Algebra Approach to Neural Associative Memories and Noise Performance of Neural Classifiers. IEEE Trans. Computers 40(12): 1429-1435 (1991) - [j8]Deming N. Zhou, Vladimir Cherkassky, T. R. Baldwin, D. E. Olson:
A neural network approach to job-shop scheduling. IEEE Trans. Neural Networks 2(1): 175-179 (1991) - [j7]Saleem Mohideen, Vladimir Cherkassky:
On recursive calculation of the generalized inverse of a matrix. ACM Trans. Math. Softw. 17(1): 130-147 (1991) - [c10]Vladimir Cherkassky, Reza Rooholamini, Hossein Lari-Najafi:
Fault-Tolerant Communications Processing. FTCS 1991: 344-351 - 1990
- [j6]Vladimir Cherkassky, Hossein Lari-Najafi, Norman L. Lawrie, Derek Masson, David W. Pritty:
Performance of a new LAN for real-time traffic. Comput. Commun. 13(5): 259-266 (1990) - [c9]Vladimir Cherkassky, Nikolaos Vassilas, Gregory L. Brodt:
Conventional and associative memory-based spelling checkers. TAI 1990: 138-144 - [c8]Deming N. Zhou, Vladimir Cherkassky, T. R. Baldwin, D. W. Hong:
Scaling neural network for job-shop scheduling. IJCNN 1990: 889-894
1980 – 1989
- 1989
- [j5]Vladimir Cherkassky, Miroslaw Malek:
Partitioning and Permuting Properties of CC-Banyan Networks. IEEE Trans. Computers 38(2): 274-278 (1989) - 1988
- [j4]Jois Malathi Char, Vladimir Cherkassky, Harry Wechsler, George Lee Zimmerman:
Distributed and Fault-Tolerant Computation for Retrieval Tasks Using Distributed Associative Memories. IEEE Trans. Computers 37(4): 484-490 (1988) - [j3]Vladimir Cherkassky:
Performance Evaluation of Neurectangular Multistage Interconnection Networks. IEEE Trans. Computers 37(10): 1269-1272 (1988) - [j2]Vladimir Cherkassky, Ross Smith:
Efficient mapping and implementation of matrix algorithms on a hypercube. J. Supercomput. 2(1): 7-27 (1988) - [c7]Vladimir Cherkassky, Hossein Lari-Najafi, Norman L. Lawrie, Derek Masson, David W. Pritty:
An Architectural Development and Performance of a Real Time LAN. ICDCS 1988: 189-196 - 1987
- [c6]Vladimir Cherkassky:
A Coding Scheme for Concurrent Error Detection/Correction In Multistage Interconnection Networks. ICPP 1987: 755-758 - [c5]Vladimir Cherkassky, Miroslaw Malek:
Graceful Degradation of Multiprocessor Systems. ICPP 1987: 885-888 - 1986
- [c4]Vladimir Cherkassky:
Performance of Non-Rectangular Multistage Interconnection Networks. ICDCS 1986: 2-7 - [c3]Vladimir Cherkassky, Miroslaw Malek:
Analysis of CC-Banyan Networks. ICPP 1986: 115 - [c2]Vladimir Cherkassky, Larry L. Kinney:
A Group Probing Strategy for Testing Large Number of Chips. ITC 1986: 853-856 - 1985
- [j1]Vladimir Cherkassky, Miroslaw Malek:
On Permuting Properties of Regular Rectangular SW-Banyans. IEEE Trans. Computers 34(6): 542-546 (1985) - [c1]Vladimir Cherkassky, Miroslaw Malek, G. Jack Lipovski:
Fail-Softness Analysis of Tree-Based Local Area Networks. ICDCS 1985: 380-385
Coauthor Index
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