Vladimir Cherkassky

116 papers A* 3A 6B 29C 3Journal 55Unranked 20

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	VC-Theoretical Explanation of Double Descent. The Importance of Being Learnable Vladimir Cherkassky, Eng Hock Lee
2024	J	`jnl`	A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition. CoRR Vladimir Cherkassky, Eng Hock Lee
2024	J	`jnl`	To understand double descent, we need to understand VC theory. Neural Networks Vladimir Cherkassky, Eng Hock Lee
2024	J	`jnl`	Understanding Double Descent Using VC-Theoretical Framework. IEEE Trans. Neural Networks Learn. Syst. Eng Hock Lee, Vladimir Cherkassky
2022	J	`jnl`	VC Theoretical Explanation of Double Descent. CoRR Eng Hock Lee, Vladimir Cherkassky
2020	J	`jnl`	Performance metrics for online seizure prediction. Neural Networks Hsiang-Han Chen, Vladimir Cherkassky
2019	B	`conf`	Group Learning for High-Dimensional Sparse Data. IJCNN Vladimir Cherkassky, Hsiang-Han Chen, Han-Tai Shiao
2019	A*	`conf`	Multiclass Learning from Contradictions. NeurIPS Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
2019	J	`jnl`	Single Class Universum-SVM. CoRR Sauptik Dhar, Vladimir Cherkassky
2018	J	`jnl`	Multiclass Universum SVM. CoRR Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
2018	J	`jnl`	Universum Selection for Boosting the Performance of Multiclass Support Vector Machines Based on One-versus-One Strategy. Knowl. Based Syst. Patoomsiri Songsiri, Vladimir Cherkassky, Boonserm Kijsirikul
2017	J	`jnl`	Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function. NeuroImage Ying Yang, Jing Wang, Cyntia Bailer, Vladimir Cherkassky, Marcel Adam Just
2017	J	`jnl`	Neural representations of the concepts in simple sentences: Concept activation prediction and context effects. NeuroImage Marcel Adam Just, Jing Wang, Vladimir Cherkassky
2017	J	`jnl`	SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal. IEEE Trans. Biomed. Eng. Han-Tai Shiao, Vladimir Cherkassky, Jieun Lee, Brandon Veber, Edward E. Patterson, Benjamin H. Brinkmann, Gregory A. Worrell
2017	B	`conf`	Universum learning for SVM regression. IJCNN Sauptik Dhar, Vladimir Cherkassky
2016	J	`jnl`	Universum Learning for Multiclass SVM. CoRR Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah
2016	J	`jnl`	Universum Learning for SVM Regression. CoRR Sauptik Dhar, Vladimir Cherkassky
2015	J	`jnl`	Development and Evaluation of Cost-Sensitive Universum-SVM. IEEE Trans. Cybern. Sauptik Dhar, Vladimir Cherkassky
2015	C	`conf`	Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM. ICMLA Adarsh Sivasankaran, Vladimir Cherkassky, Mark Albrecht, Eric Williams, Martin Maiers
2015	B	`conf`	Reliable seizure prediction from EEG data. IJCNN Vladimir Cherkassky, Brandon Veber, Jieun Lee, Han-Tai Shiao, Ned Patterson, Gregory A. Worrell, Benjamin H. Brinkmann
2014	B	`conf`	Learning using privileged information (LUPI) for modeling survival data. IJCNN Han-Tai Shiao, Vladimir Cherkassky
2012	—	`conf`	Cost-Sensitive Universum-SVM. ICMLA (1) Sauptik Dhar, Vladimir Cherkassky
2012	J	`jnl`	Generalized SMO Algorithm for SVM-Based Multitask Learning. IEEE Trans. Neural Networks Learn. Syst. Feng Cai, Vladimir Cherkassky
2012	B	`conf`	Implementation and comparison of SVM-based Multi-Task Learning methods. IJCNN Han-Tai Shiao, Vladimir Cherkassky
2012	—	`conf`	Predictive Learning, Knowledge Discovery and Philosophy of Science. WCCI Vladimir Cherkassky
2011	B	`conf`	Application of SOM to analysis of Minnesota soil survey data. IJCNN Sauptik Dhar, Vladimir Cherkassky
2011	J	`jnl`	Practical Conditions for Effectiveness of the Universum Learning. IEEE Trans. Neural Networks Vladimir Cherkassky, Sauptik Dhar, Wuyang Dai
2011	B	`conf`	Prediction of electric power consumption for commercial buildings. IJCNN Vladimir Cherkassky, Sohini Roy Chowdhury, Volker Landenberger, Saurabh Tewari, Paul Bursch
2010	—	`conf`	Simple Method for Interpretation of High-Dimensional Nonlinear SVM Classification Models. DMIN Vladimir Cherkassky, Sauptik Dhar
2009	J	`jnl`	Another look at statistical learning theory and regularization. Neural Networks Vladimir Cherkassky, Yunqian Ma
2009	—	`conf`	Empirical Study of the Universum SVM Learning for High-Dimensional Data. ICANN (1) Vladimir Cherkassky, Wuyang Dai
2009	B	`conf`	Predictive learning with sparse heterogeneous data. IJCNN Vladimir Cherkassky, Feng Cai, Lichen Liang
2009	J	`jnl`	Predictive learning with structured (grouped) data. Neural Networks Lichen Liang, Feng Cai, Vladimir Cherkassky
2009	—	`conf`	Quantitative modeling of the neural representation of adjective-noun phrases to account for fMRI activation. ACL/IJCNLP Kai-min Kevin Chang, Vladimir Cherkassky, Tom M. Mitchell, Marcel Adam Just
2009	B	`conf`	SVM+ regression and multi-task learning. IJCNN Feng Cai, Vladimir Cherkassky
2008	B	`conf`	Connection between SVM+ and multi-task learning. IJCNN Lichen Liang, Vladimir Cherkassky
2008	B	`conf`	Gender classification of human faces using inference through contradictions. IJCNN Xue Bai, Vladimir Cherkassky
2007	J	`jnl`	Computational intelligence in earth and environmental sciences. Neural Networks Vladimir Cherkassky, William Hsieh, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés
2007	B	`conf`	Learning Using Structured Data: Application to fMRI Data Analysis. IJCNN Lichen Liang, Vladimir Cherkassky
2007	A	`conf`	Probabilistic Joint Feature Selection for Multi-task Learning. SDM Tao Xiong, Jinbo Bi, R. Bharat Rao, Vladimir Cherkassky
2006	J	`jnl`	2006 Special issue: Earth Sciences and Environmental Applications of Computational IntelligenceIntroduction. Neural Networks Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés
2006	J	`jnl`	Computational intelligence in earth sciences and environmental applications: Issues and challenges. Neural Networks Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés
2006	A	`conf`	Efficient model selection for regularized linear discriminant analysis. CIKM Jieping Ye, Tao Xiong, Qi Li, Ravi Janardan, Jinbo Bi, Vladimir Cherkassky, Chandra Kambhamettu
2006	—	`conf`	Kernel Uncorrelated and Orthogonal Discriminant Analysis: A Unified Approach. CVPR (1) Tao Xiong, Jieping Ye, Vladimir Cherkassky
2006	B	`conf`	Real-Time Pricing of Mutual Funds. IJCNN Hui Gao, Vladimir Cherkassky
2006	B	`conf`	Spatial SVM for feature selection and fMRI activation detection. IJCNN Lichen Liang, Vladimir Cherkassky, David A. Rottenberg
2005	B	`conf`	A combined SVM and LDA approach for classification. IJCNN Tao Xiong, Vladimir Cherkassky
2005	B	`conf`	Characterization of data complexity for SVM methods. IJCNN Yunqian Ma, Vladimir Cherkassky
2005	J	`jnl`	Functional connectivity in an fMRI working memory task in high-functioning autism. NeuroImage Hideya Koshino, Patricia A. Carpenter, Nancy J. Minshew, Vladimir Cherkassky, Timothy A. Keller, Marcel Adam Just
2005	J	`jnl`	Multiple model regression estimation. IEEE Trans. Neural Networks Vladimir Cherkassky, Yunqian Ma
2005	B	`conf`	Special session panel discussion: methodological issues in the application of learning methods to climate modeling and Earth sciences. IJCNN Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés
2005	—	`conf`	Support vector machines and regularization. SIP Vladimir Cherkassky, Yunqian Ma
2005	J	`jnl`	Support vector machines for temporal classification of block design fMRI data. NeuroImage Stephen LaConte, Stephen C. Strother, Vladimir Cherkassky, Jon R. Anderson, Xiaoping Hu
2004	B	`conf`	Comparison of loss functions for linear regression. IJCNN Vladimir Cherkassky, Yunqian Ma
2004	—	`conf`	Efficient Kernel Discriminant Analysis via QR Decomposition. NIPS Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladimir Cherkassky
2004	J	`jnl`	Motion Estimation Using Statistical Learning Theory. IEEE Trans. Pattern Anal. Mach. Intell. Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Cherkassky
2004	B	`conf`	Multiple regression estimation for motion analysis and segmentation. IJCNN Vladimir Cherkassky, Yunqian Ma, Harry Wechsler
2004	J	`jnl`	Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks Vladimir Cherkassky, Yunqian Ma
2003	J	`jnl`	Comparison of Model Selection for Regression. Neural Comput. Vladimir Cherkassky, Yunqian Ma
2003	A*	`conf`	Controlling Model Complexity in Flow Estimation. ICCV Zoran Duric, Fayin Li, Harry Wechsler, Vladimir Cherkassky
2003	B	`conf`	Model selection for k-nearest neighbors regression using VC bounds. IJCNN Vladimir Cherkassky, Yunqian Ma, Jun Tang
2003	B	`conf`	Multiple model classification using SVM-based approach. IJCNN Yunqian Ma, Vladimir Cherkassky
2002	J	`jnl`	Model complexity control and statistical learning theory. Nat. Comput. Vladimir Cherkassky
2002	—	`conf`	Motion Prediction Using VC-Generalization Bounds. ICPR (1) Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Cherkassky
2002	C	`conf`	Selection of Meta-parameters for Support Vector Regression. ICANN Vladimir Cherkassky, Yunqian Ma
2001	C	`conf`	Comparison of Wavelet Thresholding Methods for Denoising ECG Signals. ICANN Vladimir Cherkassky, Steven Kilts
2001	J	`jnl`	Myopotential denoising of ECG signals using wavelet thresholding methods. Neural Networks Vladimir Cherkassky, Steven Kilts
2001	J	`jnl`	Signal estimation and denoising using VC-theory. Neural Networks Vladimir Cherkassky, Xuhui Shao
2000	—	`conf`	Funds exchange: an approach for risk and portfolio management. CIFEr Vladimir Cherkassky, Filip Mulier, Anna B. Sheng
2000	B	`conf`	Image Denoising Using Wavelet Thresholding and Model Selection. ICIP Shi Zhong, Vladimir Cherkassky
2000	—	`conf`	Introduction to VC learning theory with applications to Financial Engineering - Tutorial. CIFEr Vladimir Cherkassky
2000	J	`jnl`	Measuring the VC-Dimension Using Optimized Experimental Design. Neural Comput. Xuhui Shao, Vladimir Cherkassky, William Li
2000	J	`jnl`	Self-organizing maps for the skeletonization of sparse shapes. IEEE Trans. Neural Networks Learn. Syst. Rahul Singh, Vladimir Cherkassky, Nikolaos Papanikolopoulos
1999	B	`conf`	Factors controlling generalization ability of MLP networks. IJCNN Shi Zhong, Vladimir Cherkassky
1999	J	`jnl`	Model complexity control for regression using VC generalization bounds. IEEE Trans. Neural Networks Vladimir Cherkassky, Xuhui Shao, Filip Mulier, Vladimir Vapnik
1999	B	`conf`	Multi-resolution support vector machine. IJCNN Xuhui Shao, Vladimir Cherkassky
1998	A*	`conf`	Object Skeletons from Sparse Shapes in Industrial image Settings. ICRA Rahul Singh, Nikolaos Papanikolopoulos, Vladimir Cherkassky
1997	—	`conf`	Constrained Topological Maps for Regression and Classification. ICONIP (1) Vladimir Cherkassky, Y. Kim, Filip Mulier
1997	—	`conf`	Determining the skeletal description of sparse shapes. CIRA Rahul Singh, Vladimir Cherkassky, Nikolaos P. Papanikolopoulos
1997	J	`jnl`	The Nature Of Statistical Learning Theory. IEEE Trans. Neural Networks Vladimir Cherkassky
1996	J	`jnl`	Comparison of adaptive methods for function estimation from samples. IEEE Trans. Neural Networks Vladimir Cherkassky, Don Gehring, Filip Mulier
1995	J	`jnl`	ATM-Based Multimedia Servers. IEEE Multim. Reza Rooholamini, Vladimir Cherkassky
1995	J	`jnl`	Self-organization as an iterative kernel smoothing process. Neural Comput. Filip Mulier, Vladimir Cherkassky
1995	J	`jnl`	Statistical analysis of self-organization. Neural Networks Filip Mulier, Vladimir Cherkassky
1994	J	`jnl`	Finding the Right ATM Switch for the Market. Computer Reza Rooholamini, Vladimir Cherkassky, Mark Garver
1994	—	`conf`	Learning rate schedules for self-organizing maps. ICPR (2) Filip Mulier, Vladimir Cherkassky
1994	B	`conf`	Moving ATM Closer to Multimedia Applications. LCN Reza Rooholamini, Vladimir Cherkassky
1994	J	`jnl`	Neural Network for Control of Rearrangeable Clos Networks. Int. J. Neural Syst. Young-Keun Park, Vladimir Cherkassky
1994	J	`jnl`	Noise Performance of Linear Associative Memories. IEEE Trans. Pattern Anal. Mach. Intell. Kalavai J. Raghunath, Vladimir Cherkassky
1994	J	`jnl`	Omega network-based ATM switch with neural network-controlled bypass queueing and multiplexing. IEEE J. Sel. Areas Commun. Young-Keun Park, Vladimir Cherkassky, Gyungho Lee
1993	—	`conf`	Adaptive knot Placement for Nonparametric Regression. NIPS Hossein Lari-Najafi, Vladimir Cherkassky
1993	—	`conf`	Color Mapping Using Neural Networks. CIC D. Adkins, Vladimir Cherkassky, E. S. Olson
1993	—	`conf`	Neural network controller for rearrangeable switching networks. ICNN Young-Keun Park, Vladimir Cherkassky
1992	J	`jnl`	Data Representation for Diagnostic Neural Networks. IEEE Expert Vladimir Cherkassky, Hossein Lari-Najafi
1992	J	`jnl`	Semantic Networks and Associative Databases: Two Approaches to Knowledge Representation and Reasoning. IEEE Expert Ee-Peng Lim, Vladimir Cherkassky
1991	J	`jnl`	A neural network approach to job-shop scheduling. IEEE Trans. Neural Networks Deming N. Zhou, Vladimir Cherkassky, T. R. Baldwin, D. E. Olson
1991	J	`jnl`	Constrained topological mapping for nonparametric regression analysis. Neural Networks Vladimir Cherkassky, Hossein Lari-Najafi
1991	—	`conf`	Fault-Tolerant Communications Processing. FTCS Vladimir Cherkassky, Reza Rooholamini, Hossein Lari-Najafi
1991	J	`jnl`	Fault-tolerant database using distributed associative memories. Inf. Sci. Vladimir Cherkassky, Malathi Rao, Harry Wechsler
1991	J	`jnl`	Linear Algebra Approach to Neural Associative Memories and Noise Performance of Neural Classifiers. IEEE Trans. Computers Vladimir Cherkassky, Karen Fassett, Nikolaos Vassilas
1991	J	`jnl`	On recursive calculation of the generalized inverse of a matrix. ACM Trans. Math. Softw. Saleem Mohideen, Vladimir Cherkassky
1990	—	`conf`	Conventional and associative memory-based spelling checkers. TAI Vladimir Cherkassky, Nikolaos Vassilas, Gregory L. Brodt
1990	J	`jnl`	Performance of a new LAN for real-time traffic. Comput. Commun. Vladimir Cherkassky, Hossein Lari-Najafi, Norman L. Lawrie, Derek Masson, David W. Pritty
1990	B	`conf`	Scaling neural network for job-shop scheduling. IJCNN Deming N. Zhou, Vladimir Cherkassky, T. R. Baldwin, D. W. Hong
1989	J	`jnl`	Partitioning and Permuting Properties of CC-Banyan Networks. IEEE Trans. Computers Vladimir Cherkassky, Miroslaw Malek
1988	A	`conf`	An Architectural Development and Performance of a Real Time LAN. ICDCS Vladimir Cherkassky, Hossein Lari-Najafi, Norman L. Lawrie, Derek Masson, David W. Pritty
1988	J	`jnl`	Distributed and Fault-Tolerant Computation for Retrieval Tasks Using Distributed Associative Memories. IEEE Trans. Computers Jois Malathi Char, Vladimir Cherkassky, Harry Wechsler, George Lee Zimmerman
1988	J	`jnl`	Efficient mapping and implementation of matrix algorithms on a hypercube. J. Supercomput. Vladimir Cherkassky, Ross Smith
1988	J	`jnl`	Performance Evaluation of Neurectangular Multistage Interconnection Networks. IEEE Trans. Computers Vladimir Cherkassky
1987	B	`conf`	A Coding Scheme for Concurrent Error Detection/Correction In Multistage Interconnection Networks. ICPP Vladimir Cherkassky
1987	B	`conf`	Graceful Degradation of Multiprocessor Systems. ICPP Vladimir Cherkassky, Miroslaw Malek
1986	A	`conf`	A Group Probing Strategy for Testing Large Number of Chips. ITC Vladimir Cherkassky, Larry L. Kinney
1986	B	`conf`	Analysis of CC-Banyan Networks. ICPP Vladimir Cherkassky, Miroslaw Malek
1986	A	`conf`	Performance of Non-Rectangular Multistage Interconnection Networks. ICDCS Vladimir Cherkassky
1985	A	`conf`	Fail-Softness Analysis of Tree-Based Local Area Networks. ICDCS Vladimir Cherkassky, Miroslaw Malek, G. Jack Lipovski
1985	J	`jnl`	On Permuting Properties of Regular Rectangular SW-Banyans. IEEE Trans. Computers Vladimir Cherkassky, Miroslaw Malek