Fail-Softness Analysis of Tree-Based Local Area Networks. - Vladimir Cherkassky; Miroslaw Malek; G. Jack Lipovski (ICDCS, 1985)

On Permuting Properties of Regular Rectangular SW-Banyans. - Vladimir Cherkassky; Miroslaw Malek (IEEE Trans. Computers, 1985)

Performance of Non-Rectangular Multistage Interconnection Networks. - Vladimir Cherkassky (ICDCS, 1986)

Analysis of CC-Banyan Networks. - Vladimir Cherkassky; Miroslaw Malek (ICPP, 1986)

A Group Probing Strategy for Testing Large Number of Chips. - Vladimir Cherkassky; Larry L. Kinney (ITC, 1986)

A Coding Scheme for Concurrent Error Detection/Correction In Multistage Interconnection Networks. - Vladimir Cherkassky (ICPP, 1987)

Graceful Degradation of Multiprocessor Systems. - Vladimir Cherkassky; Miroslaw Malek (ICPP, 1987)

An Architectural Development and Performance of a Real Time LAN. - Vladimir Cherkassky; Hossein Lari-Najafi; Norman L. Lawrie; Derek Masson; David W. Pritty (ICDCS, 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)

Performance Evaluation of Neurectangular Multistage Interconnection Networks. - Vladimir Cherkassky (IEEE Trans. Computers, 1988)

Efficient mapping and implementation of matrix algorithms on a hypercube. - Vladimir Cherkassky; Ross Smith 004 (J. Supercomput., 1988)

Partitioning and Permuting Properties of CC-Banyan Networks. - Vladimir Cherkassky; Miroslaw Malek (IEEE Trans. Computers, 1989)

Conventional and associative memory-based spelling checkers. - Vladimir Cherkassky; Nikolaos Vassilas; Gregory L. Brodt (TAI, 1990)

Scaling neural network for job-shop scheduling. - Deming N. Zhou; Vladimir Cherkassky; T. R. Baldwin; D. W. Hong (IJCNN, 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)

Fault-Tolerant Communications Processing. - Vladimir Cherkassky; Reza Rooholamini; Hossein Lari-Najafi (FTCS, 1991)

Fault-tolerant database using distributed associative memories. - Vladimir Cherkassky; Malathi Rao; Harry Wechsler (Inf. Sci., 1991)

Constrained topological mapping for nonparametric regression analysis. - Vladimir Cherkassky; Hossein Lari-Najafi (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)

A neural network approach to job-shop scheduling. - Deming N. Zhou; Vladimir Cherkassky; T. R. Baldwin; D. E. Olson (IEEE Trans. Neural Networks, 1991)

On recursive calculation of the generalized inverse of a matrix. - Saleem Mohideen; Vladimir Cherkassky (ACM Trans. Math. Softw., 1991)

Data Representation for Diagnostic Neural Networks. - Vladimir Cherkassky; Hossein Lari-Najafi (IEEE Expert, 1992)

Semantic Networks and Associative Databases: Two Approaches to Knowledge Representation and Reasoning. - Ee-Peng Lim; Vladimir Cherkassky (IEEE Expert, 1992)

Neural network controller for rearrangeable switching networks. - Young-Keun Park; Vladimir Cherkassky (ICNN, 1993)

Color Mapping Using Neural Networks. - D. Adkins; Vladimir Cherkassky; E. S. Olson (CIC, 1993)

Adaptive knot Placement for Nonparametric Regression. - Hossein Lari-Najafi; Vladimir Cherkassky (NIPS, 1993)

Learning rate schedules for self-organizing maps. - Filip Mulier; Vladimir Cherkassky (ICPR (2), 1994)

Moving ATM Closer to Multimedia Applications. - Reza Rooholamini; Vladimir Cherkassky (LCN, 1994)

Finding the Right ATM Switch for the Market. - Reza Rooholamini; Vladimir Cherkassky; Mark Garver (Computer, 1994)

Neural Network for Control of Rearrangeable Clos Networks. - Young-Keun Park; Vladimir Cherkassky (Int. J. Neural Syst., 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)

Noise Performance of Linear Associative Memories. - Kalavai J. Raghunath; Vladimir Cherkassky (IEEE Trans. Pattern Anal. Mach. Intell., 1994)

ATM-Based Multimedia Servers. - Reza Rooholamini; Vladimir Cherkassky (IEEE Multim., 1995)

Self-organization as an iterative kernel smoothing process. - Filip Mulier; Vladimir Cherkassky (Neural Comput., 1995)

Statistical analysis of self-organization. - Filip Mulier; Vladimir Cherkassky (Neural Networks, 1995)

Comparison of adaptive methods for function estimation from samples. - Vladimir Cherkassky; Don Gehring; Filip Mulier (IEEE Trans. Neural Networks, 1996)

Determining the skeletal description of sparse shapes. - Rahul Singh; Vladimir Cherkassky; Nikolaos P. Papanikolopoulos (CIRA, 1997)

Constrained Topological Maps for Regression and Classification. - Vladimir Cherkassky; Y. Kim; Filip Mulier (ICONIP (1), 1997)

The Nature Of Statistical Learning Theory. - Vladimir Cherkassky (IEEE Trans. Neural Networks, 1997)

Object Skeletons from Sparse Shapes in Industrial image Settings. - Rahul Singh; Nikolaos Papanikolopoulos; Vladimir Cherkassky (ICRA, 1998)

Multi-resolution support vector machine. - Xuhui Shao; Vladimir Cherkassky (IJCNN, 1999)

Factors controlling generalization ability of MLP networks. - Shi Zhong 001; Vladimir Cherkassky (IJCNN, 1999)

Model complexity control for regression using VC generalization bounds. - Vladimir Cherkassky; Xuhui Shao; Filip Mulier; Vladimir Vapnik (IEEE Trans. Neural Networks, 1999)

Introduction to VC learning theory with applications to Financial Engineering - Tutorial. - Vladimir Cherkassky (CIFEr, 2000)

Funds exchange: an approach for risk and portfolio management. - Vladimir Cherkassky; Filip Mulier; Anna B. Sheng (CIFEr, 2000)

Image Denoising Using Wavelet Thresholding and Model Selection. - Shi Zhong 001; Vladimir Cherkassky (ICIP, 2000)

Measuring the VC-Dimension Using Optimized Experimental Design. - Xuhui Shao; Vladimir Cherkassky; William Li (Neural Comput., 2000)

Self-organizing maps for the skeletonization of sparse shapes. - Rahul Singh; Vladimir Cherkassky; Nikolaos Papanikolopoulos (IEEE Trans. Neural Networks Learn. Syst., 2000)

Comparison of Wavelet Thresholding Methods for Denoising ECG Signals. - Vladimir Cherkassky; Steven Kilts (ICANN, 2001)

Myopotential denoising of ECG signals using wavelet thresholding methods. - Vladimir Cherkassky; Steven Kilts (Neural Networks, 2001)

Signal estimation and denoising using VC-theory. - Vladimir Cherkassky; Xuhui Shao (Neural Networks, 2001)

Selection of Meta-parameters for Support Vector Regression. - Vladimir Cherkassky; Yunqian Ma (ICANN, 2002)

Motion Prediction Using VC-Generalization Bounds. - Harry Wechsler; Zoran Duric; Fayin Li; Vladimir Cherkassky (ICPR (1), 2002)

Model complexity control and statistical learning theory. - Vladimir Cherkassky (Nat. Comput., 2002)

Controlling Model Complexity in Flow Estimation. - Zoran Duric; Fayin Li; Harry Wechsler; Vladimir Cherkassky (ICCV, 2003)

Model selection for k-nearest neighbors regression using VC bounds. - Vladimir Cherkassky; Yunqian Ma; Jun Tang (IJCNN, 2003)

Multiple model classification using SVM-based approach. - Yunqian Ma; Vladimir Cherkassky (IJCNN, 2003)

Comparison of Model Selection for Regression. - Vladimir Cherkassky; Yunqian Ma (Neural Comput., 2003)

Comparison of loss functions for linear regression. - Vladimir Cherkassky; Yunqian Ma (IJCNN, 2004)

Multiple regression estimation for motion analysis and segmentation. - Vladimir Cherkassky; Yunqian Ma; Harry Wechsler (IJCNN, 2004)

Efficient Kernel Discriminant Analysis via QR Decomposition. - Tao Xiong; Jieping Ye; Qi Li 001; Ravi Janardan; Vladimir Cherkassky (NIPS, 2004)

Practical selection of SVM parameters and noise estimation for SVM regression. - Vladimir Cherkassky; Yunqian Ma (Neural Networks, 2004)

Motion Estimation Using Statistical Learning Theory. - Harry Wechsler; Zoran Duric; Fayin Li; Vladimir Cherkassky (IEEE Trans. Pattern Anal. Mach. Intell., 2004)

Special session panel discussion: methodological issues in the application of learning methods to climate modeling and Earth sciences. - Vladimir Cherkassky; Vladimir M. Krasnopolsky; Dimitri P. Solomatine; Julio J. Valdés (IJCNN, 2005)

Characterization of data complexity for SVM methods. - Yunqian Ma; Vladimir Cherkassky (IJCNN, 2005)

A combined SVM and LDA approach for classification. - Tao Xiong; Vladimir Cherkassky (IJCNN, 2005)

Support vector machines and regularization. - Vladimir Cherkassky; Yunqian Ma (SIP, 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 for temporal classification of block design fMRI data. - Stephen LaConte; Stephen C. Strother; Vladimir Cherkassky; Jon R. Anderson; Xiaoping Hu 001 (NeuroImage, 2005)

Multiple model regression estimation. - Vladimir Cherkassky; Yunqian Ma (IEEE Trans. Neural Networks, 2005)

Efficient model selection for regularized linear discriminant analysis. - Jieping Ye; Tao Xiong; Qi Li 001; Ravi Janardan; Jinbo Bi; Vladimir Cherkassky; Chandra Kambhamettu (CIKM, 2006)

Kernel Uncorrelated and Orthogonal Discriminant Analysis: A Unified Approach. - Tao Xiong; Jieping Ye; Vladimir Cherkassky (CVPR (1), 2006)

Real-Time Pricing of Mutual Funds. - Hui Gao; Vladimir Cherkassky (IJCNN, 2006)

Spatial SVM for feature selection and fMRI activation detection. - Lichen Liang; Vladimir Cherkassky; David A. Rottenberg (IJCNN, 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)

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)

Learning Using Structured Data: Application to fMRI Data Analysis. - Lichen Liang; Vladimir Cherkassky (IJCNN, 2007)

Probabilistic Joint Feature Selection for Multi-task Learning. - Tao Xiong; Jinbo Bi; R. Bharat Rao; Vladimir Cherkassky (SDM, 2007)

Computational intelligence in earth and environmental sciences. - Vladimir Cherkassky; William Hsieh; Vladimir M. Krasnopolsky; Dimitri P. Solomatine; Julio J. Valdés (Neural Networks, 2007)

Gender classification of human faces using inference through contradictions. - Xue Bai; Vladimir Cherkassky (IJCNN, 2008)

Connection between SVM+ and multi-task learning. - Lichen Liang; Vladimir Cherkassky (IJCNN, 2008)

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 (ACL/IJCNLP, 2009)

Empirical Study of the Universum SVM Learning for High-Dimensional Data. - Vladimir Cherkassky; Wuyang Dai (ICANN (1), 2009)

SVM+ regression and multi-task learning. - Feng Cai; Vladimir Cherkassky (IJCNN, 2009)

Predictive learning with sparse heterogeneous data. - Vladimir Cherkassky; Feng Cai; Lichen Liang (IJCNN, 2009)

Another look at statistical learning theory and regularization. - Vladimir Cherkassky; Yunqian Ma (Neural Networks, 2009)

Predictive learning with structured (grouped) data. - Lichen Liang; Feng Cai; Vladimir Cherkassky (Neural Networks, 2009)

Simple Method for Interpretation of High-Dimensional Nonlinear SVM Classification Models. - Vladimir Cherkassky; Sauptik Dhar (DMIN, 2010)

Prediction of electric power consumption for commercial buildings. - Vladimir Cherkassky; Sohini Roy Chowdhury; Volker Landenberger; Saurabh Tewari; Paul Bursch (IJCNN, 2011)

Application of SOM to analysis of Minnesota soil survey data. - Sauptik Dhar; Vladimir Cherkassky (IJCNN, 2011)

Practical Conditions for Effectiveness of the Universum Learning. - Vladimir Cherkassky; Sauptik Dhar; Wuyang Dai (IEEE Trans. Neural Networks, 2011)

Cost-Sensitive Universum-SVM. - Sauptik Dhar; Vladimir Cherkassky (ICMLA (1), 2012)

Implementation and comparison of SVM-based Multi-Task Learning methods. - Han-Tai Shiao; Vladimir Cherkassky (IJCNN, 2012)

Predictive Learning, Knowledge Discovery and Philosophy of Science. - Vladimir Cherkassky (WCCI, 2012)

Generalized SMO Algorithm for SVM-Based Multitask Learning. - Feng Cai; Vladimir Cherkassky (IEEE Trans. Neural Networks Learn. Syst., 2012)

Learning using privileged information (LUPI) for modeling survival data. - Han-Tai Shiao; Vladimir Cherkassky (IJCNN, 2014)

Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM. - Adarsh Sivasankaran; Vladimir Cherkassky; Mark Albrecht; Eric Williams; Martin Maiers (ICMLA, 2015)

Reliable seizure prediction from EEG data. - Vladimir Cherkassky; Brandon Veber; Jieun Lee; Han-Tai Shiao; Ned Patterson; Gregory A. Worrell; Benjamin H. Brinkmann (IJCNN, 2015)

Development and Evaluation of Cost-Sensitive Universum-SVM. - Sauptik Dhar; Vladimir Cherkassky (IEEE Trans. Cybern., 2015)

Universum Learning for Multiclass SVM. - Sauptik Dhar; Naveen Ramakrishnan; Vladimir Cherkassky; Mohak Shah (CoRR, 2016)

Universum learning for SVM regression. - Sauptik Dhar; Vladimir Cherkassky (IJCNN, 2017)

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects. - Marcel Adam Just; Jing Wang 020; Vladimir Cherkassky (NeuroImage, 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 007; Jing Wang 020; Cyntia Bailer; Vladimir Cherkassky; Marcel Adam Just (NeuroImage, 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)

Multiclass Universum SVM. - Sauptik Dhar; Vladimir Cherkassky; Mohak Shah (CoRR, 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)

Group Learning for High-Dimensional Sparse Data. - Vladimir Cherkassky; Hsiang-Han Chen; Han-Tai Shiao (IJCNN, 2019)

Multiclass Learning from Contradictions. - Sauptik Dhar; Vladimir Cherkassky; Mohak Shah (NeurIPS, 2019)

Single Class Universum-SVM. - Sauptik Dhar; Vladimir Cherkassky (CoRR, 2019)

Performance metrics for online seizure prediction. - Hsiang-Han Chen; Vladimir Cherkassky (Neural Networks, 2020)

VC Theoretical Explanation of Double Descent. - Eng Hock Lee; Vladimir Cherkassky (CoRR, 2022)

A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition. - Vladimir Cherkassky; Eng Hock Lee (CoRR, 2024)

To understand double descent, we need to understand VC theory. - Vladimir Cherkassky; Eng Hock Lee (Neural Networks, 2024)

Understanding Double Descent Using VC-Theoretical Framework. - Eng Hock Lee; Vladimir Cherkassky (IEEE Trans. Neural Networks Learn. Syst., 2024)