Vladimir Cherkassky

According to our database, Vladimir Cherkassky authored at least 106 papers between 1985 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

To understand double descent, we need to understand VC theory.

Vladimir Cherkassky, Eng Hock Lee

Neural Networks, January, 2024

A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition.

Vladimir Cherkassky, Eng Hock Lee

CoRR, 2024

2022

VC Theoretical Explanation of Double Descent.

Eng Hock Lee, Vladimir Cherkassky

CoRR, 2022

2020

Performance metrics for online seizure prediction.

Hsiang-Han Chen, Vladimir Cherkassky

Neural Networks, 2020

2019

Single Class Universum-SVM.

Sauptik Dhar, Vladimir Cherkassky

CoRR, 2019

Multiclass Learning from Contradictions.

Sauptik Dhar, Vladimir Cherkassky, Mohak Shah

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Group Learning for High-Dimensional Sparse Data.

Vladimir Cherkassky, Hsiang-Han Chen, Han-Tai Shiao

Proceedings of the International Joint Conference on Neural Networks, 2019

2018

Universum Selection for Boosting the Performance of Multiclass Support Vector Machines Based on One-versus-One Strategy.

Patoomsiri Songsiri, Vladimir Cherkassky, Boonserm Kijsirikul

Knowl. Based Syst., 2018

Multiclass Universum SVM.

Sauptik Dhar, Vladimir Cherkassky, Mohak Shah

CoRR, 2018

2017

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.

Han-Tai Shiao, Vladimir Cherkassky, Jieun Lee, Brandon Veber, Edward E. Patterson, Benjamin H. Brinkmann, Gregory A. Worrell

IEEE Trans. Biomed. Eng., 2017

Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function.

Ying Yang, Jing Wang, Cyntia Bailer, Vladimir Cherkassky, Marcel Adam Just

NeuroImage, 2017

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

Marcel Adam Just, Jing Wang, Vladimir Cherkassky

NeuroImage, 2017

Universum learning for SVM regression.

Sauptik Dhar, Vladimir Cherkassky

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016

Universum Learning for Multiclass SVM.

Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah

CoRR, 2016

2015

Development and Evaluation of Cost-Sensitive Universum-SVM.

Sauptik Dhar, Vladimir Cherkassky

IEEE Trans. Cybern., 2015

Reliable seizure prediction from EEG data.

Vladimir Cherkassky, Brandon Veber, Jieun Lee, Han-Tai Shiao, Ned Patterson, Gregory A. Worrell, Benjamin H. Brinkmann

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM.

Adarsh Sivasankaran, Vladimir Cherkassky, Mark Albrecht, Eric Williams, Martin Maiers

Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015

2014

Learning using privileged information (LUPI) for modeling survival data.

Han-Tai Shiao, Vladimir Cherkassky

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

2012

Generalized SMO Algorithm for SVM-Based Multitask Learning.

Feng Cai, Vladimir Cherkassky

IEEE Trans. Neural Networks Learn. Syst., 2012

Predictive Learning, Knowledge Discovery and Philosophy of Science.

Vladimir Cherkassky

Proceedings of the Advances in Computational Intelligence, 2012

Implementation and comparison of SVM-based Multi-Task Learning methods.

Han-Tai Shiao, Vladimir Cherkassky

Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

Cost-Sensitive Universum-SVM.

Sauptik Dhar, Vladimir Cherkassky

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

2011

Practical Conditions for Effectiveness of the Universum Learning.

Vladimir Cherkassky, Sauptik Dhar, Wuyang Dai

IEEE Trans. Neural Networks, 2011

Application of SOM to analysis of Minnesota soil survey data.

Sauptik Dhar, Vladimir Cherkassky

Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

Prediction of electric power consumption for commercial buildings.

Vladimir Cherkassky, Sohini Roy Chowdhury, Volker Landenberger, Saurabh Tewari, Paul Bursch

Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

2010

Simple Method for Interpretation of High-Dimensional Nonlinear SVM Classification Models.

Vladimir Cherkassky, Sauptik Dhar

Proceedings of The 2010 International Conference on Data Mining, 2010

2009

Predictive learning with structured (grouped) data.

Lichen Liang, Feng Cai, Vladimir Cherkassky

Neural Networks, 2009

Another look at statistical learning theory and regularization.

Vladimir Cherkassky, Yunqian Ma

Neural Networks, 2009

Predictive learning with sparse heterogeneous data.

Vladimir Cherkassky, Feng Cai, Lichen Liang

Proceedings of the International Joint Conference on Neural Networks, 2009

SVM+ regression and multi-task learning.

Feng Cai, Vladimir Cherkassky

Proceedings of the International Joint Conference on Neural Networks, 2009

Empirical Study of the Universum SVM Learning for High-Dimensional Data.

Vladimir Cherkassky, Wuyang Dai

Proceedings of the Artificial Neural Networks, 2009

Quantitative modeling of the neural representation of adjective-noun phrases to account for fMRI activation.

Kai-min Kevin Chang, Vladimir Cherkassky, Tom M. Mitchell, Marcel Adam Just

Proceedings of the ACL 2009, 2009

2008

Connection between SVM+ and multi-task learning.

Lichen Liang, Vladimir Cherkassky

Proceedings of the International Joint Conference on Neural Networks, 2008

Gender classification of human faces using inference through contradictions.

Xue Bai, Vladimir Cherkassky

Proceedings of the International Joint Conference on Neural Networks, 2008

2007

Computational intelligence in earth and environmental sciences.

Vladimir Cherkassky, William Hsieh, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés

Neural Networks, 2007

Probabilistic Joint Feature Selection for Multi-task Learning.

Tao Xiong, Jinbo Bi, R. Bharat Rao, Vladimir Cherkassky

Proceedings of the Seventh SIAM International Conference on Data Mining, 2007

Learning Using Structured Data: Application to fMRI Data Analysis.

Lichen Liang, Vladimir Cherkassky

Proceedings of the International Joint Conference on Neural Networks, 2007

2006

Computational intelligence in earth sciences and environmental applications: Issues and challenges.

Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés

Neural Networks, 2006

2006 Special issue: Earth Sciences and Environmental Applications of Computational IntelligenceIntroduction.

Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés

Neural Networks, 2006

Spatial SVM for feature selection and fMRI activation detection.

Lichen Liang, Vladimir Cherkassky, David A. Rottenberg

Proceedings of the International Joint Conference on Neural Networks, 2006

Real-Time Pricing of Mutual Funds.

Hui Gao, Vladimir Cherkassky

Proceedings of the International Joint Conference on Neural Networks, 2006

Kernel Uncorrelated and Orthogonal Discriminant Analysis: A Unified Approach.

Tao Xiong, Jieping Ye, Vladimir Cherkassky

Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

Efficient model selection for regularized linear discriminant analysis.

Jieping Ye, Tao Xiong, Qi Li, Ravi Janardan, Jinbo Bi, Vladimir Cherkassky, Chandra Kambhamettu

Proceedings of the 2006 ACM CIKM International Conference on Information and Knowledge Management, 2006

2005

Multiple model regression estimation.

Vladimir Cherkassky, Yunqian Ma

IEEE Trans. Neural Networks, 2005

Support vector machines for temporal classification of block design fMRI data.

Stephen LaConte, Stephen C. Strother, Vladimir Cherkassky, Jon R. Anderson, Xiaoping Hu

NeuroImage, 2005

Functional connectivity in an fMRI working memory task in high-functioning autism.

Hideya Koshino, Patricia A. Carpenter, Nancy J. Minshew, Vladimir Cherkassky, Timothy A. Keller, Marcel Adam Just

NeuroImage, 2005

Support vector machines and regularization.

Vladimir Cherkassky, Yunqian Ma

Proceedings of the Signal and Image Processing (SIP 2005), 2005

2004

Motion Estimation Using Statistical Learning Theory.

Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Cherkassky

IEEE Trans. Pattern Anal. Mach. Intell., 2004

Practical selection of SVM parameters and noise estimation for SVM regression.

Vladimir Cherkassky, Yunqian Ma

Neural Networks, 2004

Efficient Kernel Discriminant Analysis via QR Decomposition.

Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladimir Cherkassky

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

Comparison of Model Selection for Regression.

Vladimir Cherkassky, Yunqian Ma

Neural Comput., 2003

Controlling Model Complexity in Flow Estimation.

Zoran Duric, Fayin Li, Harry Wechsler, Vladimir Cherkassky

Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV 2003), 2003

2002

Model complexity control and statistical learning theory.

Vladimir Cherkassky

Nat. Comput., 2002

Motion Prediction Using VC-Generalization Bounds.

Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Cherkassky

Proceedings of the 16th International Conference on Pattern Recognition, 2002

Selection of Meta-parameters for Support Vector Regression.

Vladimir Cherkassky, Yunqian Ma

Proceedings of the Artificial Neural Networks, 2002

2001

Signal estimation and denoising using VC-theory.

Vladimir Cherkassky, Xuhui Shao

Neural Networks, 2001

Myopotential denoising of ECG signals using wavelet thresholding methods.

Vladimir Cherkassky, Steven Kilts

Neural Networks, 2001

Comparison of Wavelet Thresholding Methods for Denoising ECG Signals.

Vladimir Cherkassky, Steven Kilts

Proceedings of the Artificial Neural Networks, 2001

2000

Self-organizing maps for the skeletonization of sparse shapes.

Rahul Singh, Vladimir Cherkassky, Nikolaos Papanikolopoulos

IEEE Trans. Neural Networks Learn. Syst., 2000

Measuring the VC-Dimension Using Optimized Experimental Design.

Xuhui Shao, Vladimir Cherkassky, William Li

Neural Comput., 2000

Image Denoising Using Wavelet Thresholding and Model Selection.

Shi Zhong, Vladimir Cherkassky

Proceedings of the 2000 International Conference on Image Processing, 2000

Funds exchange: an approach for risk and portfolio management.

Vladimir Cherkassky, Filip Mulier, Anna B. Sheng

Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering, 2000

Introduction to VC learning theory with applications to Financial Engineering - Tutorial.

Vladimir Cherkassky

Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering, 2000

1999

Model complexity control for regression using VC generalization bounds.

Vladimir Cherkassky, Xuhui Shao, Filip Mulier, Vladimir Vapnik

IEEE Trans. Neural Networks, 1999

Factors controlling generalization ability of MLP networks.

Shi Zhong, Vladimir Cherkassky

Proceedings of the International Joint Conference Neural Networks, 1999

Multi-resolution support vector machine.

Xuhui Shao, Vladimir Cherkassky

Proceedings of the International Joint Conference Neural Networks, 1999

1998

Object Skeletons from Sparse Shapes in Industrial image Settings.

Rahul Singh, Nikolaos Papanikolopoulos, Vladimir Cherkassky

Proceedings of the IEEE International Conference on Robotics and Automation, 1998

1997

The Nature Of Statistical Learning Theory.

Vladimir Cherkassky

IEEE Trans. Neural Networks, 1997

Constrained Topological Maps for Regression and Classification.

Vladimir Cherkassky, Y. Kim, Filip Mulier

Proceedings of the Progress in Connectionist-Based Information Systems: Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, 1997

Determining the skeletal description of sparse shapes.

Rahul Singh, Vladimir Cherkassky, Nikolaos P. Papanikolopoulos

Proceedings of the Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97, 1997

1996

Comparison of adaptive methods for function estimation from samples.

Vladimir Cherkassky, Don Gehring, Filip Mulier

IEEE Trans. Neural Networks, 1996

1995

Statistical analysis of self-organization.

Filip Mulier, Vladimir Cherkassky

Neural Networks, 1995

Self-organization as an iterative kernel smoothing process.

Filip Mulier, Vladimir Cherkassky

Neural Comput., 1995

ATM-Based Multimedia Servers.

Reza Rooholamini, Vladimir Cherkassky

IEEE Multim., 1995

1994

Noise Performance of Linear Associative Memories.

Kalavai J. Raghunath, Vladimir Cherkassky

IEEE Trans. Pattern Anal. Mach. Intell., 1994

Omega network-based ATM switch with neural network-controlled bypass queueing and multiplexing.

Young-Keun Park, Vladimir Cherkassky, Gyungho Lee

IEEE J. Sel. Areas Commun., 1994

Neural Network for Control of Rearrangeable Clos Networks.

Young-Keun Park, Vladimir Cherkassky

Int. J. Neural Syst., 1994

Finding the Right ATM Switch for the Market.

Reza Rooholamini, Vladimir Cherkassky, Mark Garver

Computer, 1994

Moving ATM Closer to Multimedia Applications.

Reza Rooholamini, Vladimir Cherkassky

Proceedings of the Proceedings 19th Conference on Local Computer Networks, 1994

Learning rate schedules for self-organizing maps.

Filip Mulier, Vladimir Cherkassky

Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994

1993

Adaptive knot Placement for Nonparametric Regression.

Hossein Lari-Najafi, Vladimir Cherkassky

Proceedings of the Advances in Neural Information Processing Systems 6, 1993

Color Mapping Using Neural Networks.

D. Adkins, Vladimir Cherkassky, E. S. Olson

Proceedings of the 1st Color and Imaging Conference, 1993

Neural network controller for rearrangeable switching networks.

Young-Keun Park, Vladimir Cherkassky

Proceedings of International Conference on Neural Networks (ICNN'88), San Francisco, CA, USA, March 28, 1993

1992

Semantic Networks and Associative Databases: Two Approaches to Knowledge Representation and Reasoning.

Ee-Peng Lim, Vladimir Cherkassky

IEEE Expert, 1992

Data Representation for Diagnostic Neural Networks.

Vladimir Cherkassky, Hossein Lari-Najafi

IEEE Expert, 1992

1991

On recursive calculation of the generalized inverse of a matrix.

Saleem Mohideen, Vladimir Cherkassky

ACM Trans. Math. Softw., 1991

A neural network approach to job-shop scheduling.

Deming N. Zhou, Vladimir Cherkassky, T. R. Baldwin, D. E. Olson

IEEE Trans. Neural Networks, 1991

Linear Algebra Approach to Neural Associative Memories and Noise Performance of Neural Classifiers.

Vladimir Cherkassky, Karen Fassett, Nikolaos Vassilas

IEEE Trans. Computers, 1991

Constrained topological mapping for nonparametric regression analysis.

Vladimir Cherkassky, Hossein Lari-Najafi

Neural Networks, 1991

Fault-tolerant database using distributed associative memories.

Vladimir Cherkassky, Malathi Rao, Harry Wechsler

Inf. Sci., 1991

Fault-Tolerant Communications Processing.

Vladimir Cherkassky, Reza Rooholamini, Hossein Lari-Najafi

Proceedings of the 1991 International Symposium on Fault-Tolerant Computing, 1991

1990

Performance of a new LAN for real-time traffic.

Vladimir Cherkassky, Hossein Lari-Najafi, Norman L. Lawrie, Derek Masson, David W. Pritty

Comput. Commun., 1990

Scaling neural network for job-shop scheduling.

Deming N. Zhou, Vladimir Cherkassky, T. R. Baldwin, D. W. Hong

Proceedings of the IJCNN 1990, 1990

Conventional and associative memory-based spelling checkers.

Vladimir Cherkassky, Nikolaos Vassilas, Gregory L. Brodt

Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, 1990

1989

Partitioning and Permuting Properties of CC-Banyan Networks.

Vladimir Cherkassky, Miroslaw Malek

IEEE Trans. Computers, 1989

1988

Efficient mapping and implementation of matrix algorithms on a hypercube.

Vladimir Cherkassky, Ross Smith

J. Supercomput., 1988

Performance Evaluation of Neurectangular Multistage Interconnection Networks.

Vladimir Cherkassky

IEEE Trans. Computers, 1988

Distributed and Fault-Tolerant Computation for Retrieval Tasks Using Distributed Associative Memories.

Jois Malathi Char, Vladimir Cherkassky, Harry Wechsler, George Lee Zimmerman

IEEE Trans. Computers, 1988

An Architectural Development and Performance of a Real Time LAN.

Vladimir Cherkassky, Hossein Lari-Najafi, Norman L. Lawrie, Derek Masson, David W. Pritty

Proceedings of the 8th International Conference on Distributed Computing Systems, 1988

1987

Graceful Degradation of Multiprocessor Systems.

Vladimir Cherkassky, Miroslaw Malek

Proceedings of the International Conference on Parallel Processing, 1987

A Coding Scheme for Concurrent Error Detection/Correction In Multistage Interconnection Networks.

Vladimir Cherkassky

Proceedings of the International Conference on Parallel Processing, 1987

1986

A Group Probing Strategy for Testing Large Number of Chips.

Vladimir Cherkassky, Larry L. Kinney

Proceedings of the Proceedings International Test Conference 1986, 1986

Analysis of CC-Banyan Networks.

Vladimir Cherkassky, Miroslaw Malek

Proceedings of the International Conference on Parallel Processing, 1986

Performance of Non-Rectangular Multistage Interconnection Networks.

Vladimir Cherkassky

Proceedings of the 6th International Conference on Distributed Computing Systems, 1986

1985

On Permuting Properties of Regular Rectangular SW-Banyans.

Vladimir Cherkassky, Miroslaw Malek

IEEE Trans. Computers, 1985

Fail-Softness Analysis of Tree-Based Local Area Networks.

Vladimir Cherkassky, Miroslaw Malek, G. Jack Lipovski

Proceedings of the 5th International Conference on Distributed Computing Systems, 1985