Vladimir Cherkassky

Publication Activity (10 Years)

Years Active: 1985-2024
Publications (10 Years): 14

Top Venues

CoRR

IJCNN

NeuroImage

Neural Networks

Publications

Vladimir Cherkassky, Eng Hock Lee
To understand double descent, we need to understand VC theory. Neural Networks 169 (2024)
Eng Hock Lee, Vladimir Cherkassky
VC Theoretical Explanation of Double Descent. CoRR (2022)
Hsiang-Han Chen, Vladimir Cherkassky
Performance metrics for online seizure prediction. Neural Networks 128 (2020)
Vladimir Cherkassky, Hsiang-Han Chen, Han-Tai Shiao
Group Learning for High-Dimensional Sparse Data. IJCNN (2019)
Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
Multiclass Learning from Contradictions. NeurIPS (2019)
Sauptik Dhar, Vladimir Cherkassky
Single Class Universum-SVM. CoRR (2019)
Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
Multiclass Universum SVM. CoRR (2018)
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 (2018)
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 (2017)
Marcel Adam Just, Jing Wang, Vladimir Cherkassky
Neural representations of the concepts in simple sentences: Concept activation prediction and context effects. NeuroImage 157 (2017)
Sauptik Dhar, Vladimir Cherkassky
Universum learning for SVM regression. IJCNN (2017)
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) (2017)
Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah
Universum Learning for Multiclass SVM. CoRR (2016)
Sauptik Dhar, Vladimir Cherkassky
Universum Learning for SVM Regression. CoRR (2016)
Adarsh Sivasankaran, Vladimir Cherkassky, Mark Albrecht, Eric Williams, Martin Maiers
Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM. ICMLA (2015)
Sauptik Dhar, Vladimir Cherkassky
Development and Evaluation of Cost-Sensitive Universum-SVM. IEEE Trans. Cybern. 45 (4) (2015)
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)
Han-Tai Shiao, Vladimir Cherkassky
Learning using privileged information (LUPI) for modeling survival data. IJCNN (2014)
Han-Tai Shiao, Vladimir Cherkassky
Implementation and comparison of SVM-based Multi-Task Learning methods. IJCNN (2012)
Vladimir Cherkassky
Predictive Learning, Knowledge Discovery and Philosophy of Science. WCCI (2012)
Sauptik Dhar, Vladimir Cherkassky
Cost-Sensitive Universum-SVM. ICMLA (1) (2012)
Feng Cai, Vladimir Cherkassky
Generalized SMO Algorithm for SVM-Based Multitask Learning. IEEE Trans. Neural Networks Learn. Syst. 23 (6) (2012)
Vladimir Cherkassky, Sauptik Dhar, Wuyang Dai
Practical Conditions for Effectiveness of the Universum Learning. IEEE Trans. Neural Networks 22 (8) (2011)
Sauptik Dhar, Vladimir Cherkassky
Application of SOM to analysis of Minnesota soil survey data. IJCNN (2011)
Vladimir Cherkassky, Sohini Roy Chowdhury, Volker Landenberger, Saurabh Tewari, Paul Bursch
Prediction of electric power consumption for commercial buildings. IJCNN (2011)
Vladimir Cherkassky, Sauptik Dhar
Simple Method for Interpretation of High-Dimensional Nonlinear SVM Classification Models. DMIN (2010)
Vladimir Cherkassky, Wuyang Dai
Empirical Study of the Universum SVM Learning for High-Dimensional Data. ICANN (1) (2009)
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)
Vladimir Cherkassky, Yunqian Ma
Another look at statistical learning theory and regularization. Neural Networks 22 (7) (2009)
Lichen Liang, Feng Cai, Vladimir Cherkassky
Predictive learning with structured (grouped) data. Neural Networks 22 (5-6) (2009)
Feng Cai, Vladimir Cherkassky
SVM+ regression and multi-task learning. IJCNN (2009)
Vladimir Cherkassky, Feng Cai, Lichen Liang
Predictive learning with sparse heterogeneous data. IJCNN (2009)
Xue Bai, Vladimir Cherkassky
Gender classification of human faces using inference through contradictions. IJCNN (2008)
Lichen Liang, Vladimir Cherkassky
Connection between SVM+ and multi-task learning. IJCNN (2008)
Lichen Liang, Vladimir Cherkassky
Learning Using Structured Data: Application to fMRI Data Analysis. IJCNN (2007)
Tao Xiong, Jinbo Bi, R. Bharat Rao, Vladimir Cherkassky
Probabilistic Joint Feature Selection for Multi-task Learning. SDM (2007)
Vladimir Cherkassky, William Hsieh, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés
Computational intelligence in earth and environmental sciences. Neural Networks 20 (4) (2007)
Lichen Liang, Vladimir Cherkassky, David A. Rottenberg
Spatial SVM for feature selection and fMRI activation detection. IJCNN (2006)
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) (2006)
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) (2006)
Hui Gao, Vladimir Cherkassky
Real-Time Pricing of Mutual Funds. IJCNN (2006)
Tao Xiong, Jieping Ye, Vladimir Cherkassky
Kernel Uncorrelated and Orthogonal Discriminant Analysis: A Unified Approach. CVPR (1) (2006)
Jieping Ye, Tao Xiong, Qi Li, Ravi Janardan, Jinbo Bi, Vladimir Cherkassky, Chandra Kambhamettu
Efficient model selection for regularized linear discriminant analysis. CIKM (2006)
Vladimir Cherkassky, Yunqian Ma
Multiple model regression estimation. IEEE Trans. Neural Networks 16 (4) (2005)
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) (2005)
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) (2005)
Vladimir Cherkassky, Yunqian Ma
Support vector machines and regularization. SIP (2005)
Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Cherkassky
Motion Estimation Using Statistical Learning Theory. IEEE Trans. Pattern Anal. Mach. Intell. 26 (4) (2004)
Vladimir Cherkassky, Yunqian Ma
Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17 (1) (2004)
Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladimir Cherkassky
Efficient Kernel Discriminant Analysis via QR Decomposition. NIPS (2004)
Zoran Duric, Fayin Li, Harry Wechsler, Vladimir Cherkassky
Controlling Model Complexity in Flow Estimation. ICCV (2003)
Vladimir Cherkassky, Yunqian Ma
Comparison of Model Selection for Regression. Neural Comput. 15 (7) (2003)
Vladimir Cherkassky
Model complexity control and statistical learning theory. Nat. Comput. 1 (1) (2002)
Vladimir Cherkassky, Yunqian Ma
Selection of Meta-parameters for Support Vector Regression. ICANN (2002)
Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Cherkassky
Motion Prediction Using VC-Generalization Bounds. ICPR (1) (2002)
Vladimir Cherkassky, Steven Kilts
Myopotential denoising of ECG signals using wavelet thresholding methods. Neural Networks 14 (8) (2001)
Vladimir Cherkassky, Xuhui Shao
Signal estimation and denoising using VC-theory. Neural Networks 14 (1) (2001)
Vladimir Cherkassky, Steven Kilts
Comparison of Wavelet Thresholding Methods for Denoising ECG Signals. ICANN (2001)
Vladimir Cherkassky
Introduction to VC learning theory with applications to Financial Engineering - Tutorial. CIFEr (2000)
Vladimir Cherkassky, Filip Mulier, Anna B. Sheng
Funds exchange: an approach for risk and portfolio management. CIFEr (2000)
Xuhui Shao, Vladimir Cherkassky, William Li
Measuring the VC-Dimension Using Optimized Experimental Design. Neural Comput. 12 (8) (2000)
Rahul Singh, Vladimir Cherkassky, Nikolaos Papanikolopoulos
Self-organizing maps for the skeletonization of sparse shapes. IEEE Trans. Neural Networks Learn. Syst. 11 (1) (2000)
Shi Zhong, Vladimir Cherkassky
Image Denoising Using Wavelet Thresholding and Model Selection. ICIP (2000)
Xuhui Shao, Vladimir Cherkassky
Multi-resolution support vector machine. IJCNN (1999)
Shi Zhong, Vladimir Cherkassky
Factors controlling generalization ability of MLP networks. IJCNN (1999)
Vladimir Cherkassky, Xuhui Shao, Filip Mulier, Vladimir Vapnik
Model complexity control for regression using VC generalization bounds. IEEE Trans. Neural Networks 10 (5) (1999)
Rahul Singh, Nikolaos Papanikolopoulos, Vladimir Cherkassky
Object Skeletons from Sparse Shapes in Industrial image Settings. ICRA (1998)
Rahul Singh, Vladimir Cherkassky, Nikolaos P. Papanikolopoulos
Determining the skeletal description of sparse shapes. CIRA (1997)
Vladimir Cherkassky
The Nature Of Statistical Learning Theory. IEEE Trans. Neural Networks 8 (6) (1997)
Vladimir Cherkassky, Y. Kim, Filip Mulier
Constrained Topological Maps for Regression and Classification. ICONIP (1) (1997)
Vladimir Cherkassky, Don Gehring, Filip Mulier
Comparison of adaptive methods for function estimation from samples. IEEE Trans. Neural Networks 7 (4) (1996)
Filip Mulier, Vladimir Cherkassky
Statistical analysis of self-organization. Neural Networks 8 (5) (1995)
Reza Rooholamini, Vladimir Cherkassky
ATM-Based Multimedia Servers. IEEE Multim. 2 (1) (1995)
Filip Mulier, Vladimir Cherkassky
Self-organization as an iterative kernel smoothing process. Neural Comput. 7 (6) (1995)
Kalavai J. Raghunath, Vladimir Cherkassky
Noise Performance of Linear Associative Memories. IEEE Trans. Pattern Anal. Mach. Intell. 16 (7) (1994)
Reza Rooholamini, Vladimir Cherkassky, Mark Garver
Finding the Right ATM Switch for the Market. Computer 27 (4) (1994)
Filip Mulier, Vladimir Cherkassky
Learning rate schedules for self-organizing maps. ICPR (2) (1994)
Reza Rooholamini, Vladimir Cherkassky
Moving ATM Closer to Multimedia Applications. LCN (1994)
Young-Keun Park, Vladimir Cherkassky
Neural Network for Control of Rearrangeable Clos Networks. Int. J. Neural Syst. 5 (3) (1994)
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) (1994)
Young-Keun Park, Vladimir Cherkassky
Neural network controller for rearrangeable switching networks. ICNN (1993)
Hossein Lari-Najafi, Vladimir Cherkassky
Adaptive knot Placement for Nonparametric Regression. NIPS (1993)
D. Adkins, Vladimir Cherkassky, E. S. Olson
Color Mapping Using Neural Networks. Color Imaging Conference (1993)
Ee-Peng Lim, Vladimir Cherkassky
Semantic Networks and Associative Databases: Two Approaches to Knowledge Representation and Reasoning. IEEE Expert 7 (4) (1992)
Vladimir Cherkassky, Hossein Lari-Najafi
Data Representation for Diagnostic Neural Networks. IEEE Expert 7 (5) (1992)
Saleem Mohideen, Vladimir Cherkassky
On recursive calculation of the generalized inverse of a matrix. ACM Trans. Math. Softw. 17 (1) (1991)
Vladimir Cherkassky, Reza Rooholamini, Hossein Lari-Najafi
Fault-Tolerant Communications Processing. FTCS (1991)
Vladimir Cherkassky, Hossein Lari-Najafi
Constrained topological mapping for nonparametric regression analysis. Neural Networks 4 (1) (1991)
Vladimir Cherkassky, Karen Fassett, Nikolaos Vassilas
Linear Algebra Approach to Neural Associative Memories and Noise Performance of Neural Classifiers. IEEE Trans. Computers 40 (12) (1991)
Vladimir Cherkassky, Malathi Rao, Harry Wechsler
Fault-tolerant database using distributed associative memories. Inf. Sci. 53 (1-2) (1991)
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) (1991)
Deming N. Zhou, Vladimir Cherkassky, T. R. Baldwin, D. W. Hong
Scaling neural network for job-shop scheduling. IJCNN (1990)
Vladimir Cherkassky, Nikolaos Vassilas, Gregory L. Brodt
Conventional and associative memory-based spelling checkers. TAI (1990)
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) (1990)
Vladimir Cherkassky, Miroslaw Malek
Partitioning and Permuting Properties of CC-Banyan Networks. IEEE Trans. Computers 38 (2) (1989)
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) (1988)
Vladimir Cherkassky, Ross Smith
Efficient mapping and implementation of matrix algorithms on a hypercube. J. Supercomput. 2 (1) (1988)
Vladimir Cherkassky
Performance Evaluation of Neurectangular Multistage Interconnection Networks. IEEE Trans. Computers 37 (10) (1988)
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)
Vladimir Cherkassky
A Coding Scheme for Concurrent Error Detection/Correction In Multistage Interconnection Networks. ICPP (1987)

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Publication Activity (10 Years)

Top Topics

Top Venues

Vladimir Cherkassky