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Vladimir Cherkassky - Publications

Affiliations: 
Electrical Engineering University of Minnesota, Twin Cities, Minneapolis, MN 
Area:
Electronics and Electrical Engineering, Computer Science
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55 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2023 Cherkassky V, Lee EH. To understand double descent, we need to understand VC theory. Neural Networks : the Official Journal of the International Neural Network Society. 169: 242-256. PMID 37913656 DOI: 10.1016/j.neunet.2023.10.014  0.407
2020 Chen HH, Cherkassky V. Performance metrics for online seizure prediction. Neural Networks : the Official Journal of the International Neural Network Society. 128: 22-32. PMID 32387921 DOI: 10.1016/J.Neunet.2020.04.022  0.315
2015 Dhar S, Cherkassky V. Development and evaluation of cost-sensitive universum-SVM. Ieee Transactions On Cybernetics. 45: 806-18. PMID 25265638 DOI: 10.1109/Tcyb.2014.2336876  0.637
2015 Cherkassky V, Dhar S. Interpretation of black-box predictive models Measures of Complexity: Festschrift For Alexey Chervonenkis. 267-286. DOI: 10.1007/978-3-319-21852-6_19  0.573
2014 Shiao HT, Cherkassky V. Learning using privileged information (LUPI) for modeling survival data Proceedings of the International Joint Conference On Neural Networks. 1042-1049. DOI: 10.1109/IJCNN.2014.6889517  0.333
2014 Sivasankaran A, Madbouly A, Cherkassky V, Maiers M. 1005-LBP Human Immunology. 75: 480. DOI: 10.1016/J.Humimm.2014.01.017  0.397
2012 Cai F, Cherkassky V. Generalized SMO algorithm for SVM-based multitask learning. Ieee Transactions On Neural Networks and Learning Systems. 23: 997-1003. PMID 24806769 DOI: 10.1109/Tnnls.2012.2187307  0.626
2012 Cherkassky V. The nature of statistical learning theory~. Ieee Transactions On Neural Networks. 8: 1564. PMID 18255760 DOI: 10.1109/Tnn.1997.641482  0.374
2012 Shiao HT, Cherkassky V. Implementation and comparison of SVM-based Multi-Task Learning methods Proceedings of the International Joint Conference On Neural Networks. DOI: 10.1109/IJCNN.2012.6252442  0.401
2012 Dhar S, Cherkassky V. Cost-sensitive universum-SVM Proceedings - 2012 11th International Conference On Machine Learning and Applications, Icmla 2012. 1: 220-225. DOI: 10.1109/ICMLA.2012.45  0.602
2012 Dickson E, Jonson A, Cherkassky V, Shiao H, Downs L. Machine learning as a tool to predict survival outcomes for carcinosarcoma of the female genital tract Gynecologic Oncology. 125: S150. DOI: 10.1016/J.Ygyno.2011.12.369  0.348
2012 Cherkassky V. Predictive learning, knowledge discovery and philosophy of science Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7311: 209-233. DOI: 10.1007/978-3-642-30687-7_11  0.363
2011 Cherkassky V, Dhar S, Dai W. Practical conditions for effectiveness of the Universum learning. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 22: 1241-55. PMID 21724504 DOI: 10.1109/Tnn.2011.2157522  0.638
2011 Dhar S, Cherkassky V. Application of SOM to analysis of Minnesota soil survey data Proceedings of the International Joint Conference On Neural Networks. 633-639. DOI: 10.1109/IJCNN.2011.6033280  0.431
2009 Liang L, Cai F, Cherkassky V. Predictive learning with structured (grouped) data. Neural Networks : the Official Journal of the International Neural Network Society. 22: 766-73. PMID 19596546 DOI: 10.1016/J.Neunet.2009.06.030  0.725
2009 Cherkassky V, Ma Y. Another look at statistical learning theory and regularization. Neural Networks : the Official Journal of the International Neural Network Society. 22: 958-69. PMID 19443179 DOI: 10.1016/J.Neunet.2009.04.005  0.666
2009 Cherkassky V, Cai F, Liang L. Predictive learning with sparse heterogeneous data Proceedings of the International Joint Conference On Neural Networks. 544-551. DOI: 10.1109/IJCNN.2009.5179036  0.712
2009 Cai F, Cherkassky V. SVM+ regression and Multi-Task Learning Proceedings of the International Joint Conference On Neural Networks. 418-424. DOI: 10.1109/IJCNN.2009.5178650  0.624
2009 Cherkassky V, Dai W. Empirical study of the universum SVM learning for high-dimensional data Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5768: 932-941. DOI: 10.1007/978-3-642-04274-4_96  0.414
2008 Liang L, Cherkassky V. Connection between SVM+ and multi-task learning Proceedings of the International Joint Conference On Neural Networks. 2048-2054. DOI: 10.1109/IJCNN.2008.4634079  0.616
2008 Bai X, Cherkassky V. Gender classification of human faces using inference through contradictions Proceedings of the International Joint Conference On Neural Networks. 746-750. DOI: 10.1109/IJCNN.2008.4633879  0.351
2007 Liang L, Cherkassky V. Learning using structured data: Application to fMRI data analysis Ieee International Conference On Neural Networks - Conference Proceedings. 495-499. DOI: 10.1109/IJCNN.2007.4371006  0.627
2007 Xiong T, Bit J, Rao B, Cherkassky V. Probabilistic joint feature selection for multi-task learning Proceedings of the 7th Siam International Conference On Data Mining. 332-342.  0.333
2006 Cherkassky V, Krasnopolsky V, Solomatine DP, Valdes J. Computational intelligence in earth sciences and environmental applications: Issues and challenges Neural Networks. 19: 113-121. PMID 16527457 DOI: 10.1016/J.Neunet.2006.01.001  0.45
2006 Cherkassky V, Mulier F. Learning from Data: Concepts, Theory, and Methods: Second Edition Learning From Data: Concepts, Theory, and Methods: Second Edition. 1-538. DOI: 10.1002/9780470140529  0.341
2006 Liang L, Cherkassky V, Rottenberg DA. Spatial SVM for feature selection and fMRI activation detection Ieee International Conference On Neural Networks - Conference Proceedings. 1463-1469.  0.619
2005 Cherkassky V, Ma Y. Multiple model regression estimation. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 16: 785-98. PMID 16121721 DOI: 10.1109/Tnn.2005.849836  0.694
2005 LaConte S, Strother S, Cherkassky V, Anderson J, Hu X. Support vector machines for temporal classification of block design fMRI data. Neuroimage. 26: 317-29. PMID 15907293 DOI: 10.1016/J.Neuroimage.2005.01.048  0.377
2005 Cherkassky V, Krasnopolsky VM, Solomatine DP, Valdés JJ. Special session panel discussion: Methodological issues in the application of learning methods to climate modeling and earth sciences Proceedings of the International Joint Conference On Neural Networks. 3: 1728. DOI: 10.1109/IJCNN.2005.1556140  0.369
2005 Xiong T, Cherkassky V. A combined SVM and LDA approach for classification Proceedings of the International Joint Conference On Neural Networks. 3: 1455-1459. DOI: 10.1109/IJCNN.2005.1556089  0.539
2005 Ma Y, Cherkassky V. Characterization of data complexity for SVM methods Proceedings of the International Joint Conference On Neural Networks. 2: 919-924. DOI: 10.1109/IJCNN.2005.1555975  0.602
2005 Cherkassky V, Ma Y. Support vector machines and regularization Proceedings of the Seventh Iasted International Conference On Signal and Image Processing, Sip 2005. 166-171.  0.388
2004 Wechsler H, Duric Z, Li F, Cherkassky V. Motion estimation using Statistical Learning Theory. Ieee Transactions On Pattern Analysis and Machine Intelligence. 26: 466-78. PMID 15382651 DOI: 10.1109/Tpami.2004.1265862  0.409
2004 Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks : the Official Journal of the International Neural Network Society. 17: 113-26. PMID 14690712 DOI: 10.1016/S0893-6080(03)00169-2  0.637
2004 Cherkassky V, Ma Y, Wechsler H. Multiple regression estimation for motion analysis and segmentation Ieee International Conference On Neural Networks - Conference Proceedings. 4: 2547-2552. DOI: 10.1109/IJCNN.2004.1381043  0.589
2003 Cherkassky V, Ma Y. Comparison of model selection for regression. Neural Computation. 15: 1691-714. PMID 12816572 DOI: 10.1162/089976603321891864  0.649
2003 Duric Z, Li F, Wechsler H, Cherkassky V. Controlling model complexity in flow estimation Proceedings of the Ieee International Conference On Computer Vision. 2: 908-914.  0.348
2003 Ma Y, Cherkassky V. Multiple Model Classification Using SVM-based Approach Proceedings of the International Joint Conference On Neural Networks. 2: 1581-1586.  0.636
2003 Cherkassky V, Ma Y, Tang J. Model Selection for K-Nearest Neighbors Regression Using VC Bounds Proceedings of the International Joint Conference On Neural Networks. 2: 1143-1148.  0.584
2002 Cherkassky V. Natural Computing. 1: 109-133. DOI: 10.1023/A:1015007927558  0.445
2002 Cherkassky V, Ma Y. Selection of meta-parameters for support vector regression Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2415: 687-693.  0.605
2001 Cherkassky V, Kilts S. Myopotential denoising of ECG signals using wavelet thresholding methods Neural Networks. 14: 1129-1137. PMID 11681756 DOI: 10.1016/S0893-6080(01)00041-7  0.347
2001 Cherkassky V, Shao X. Signal estimation and denoising using VC-theory Neural Networks. 14: 37-52. PMID 11213212 DOI: 10.1016/S0893-6080(00)00078-2  0.403
2000 Shao X, Cherkassky V, Li W. Measuring the VC-dimension using optimized experimental design Neural Computation. 12: 1969-1986. PMID 10953247 DOI: 10.1162/089976600300015222  0.371
1999 Cherkassky V, Shao X, Mulier FM, Vapnik VN. Model complexity control for regression using VC generalization bounds. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 10: 1075-89. PMID 18252610 DOI: 10.1109/72.788648  0.442
1999 Cherkassky V, Mulier F. Vapnik-Chervonenkis (VC) learning theory and its applications Ieee Transactions On Neural Networks. 10: 985-987. DOI: 10.1109/Tnn.1999.788639  0.418
1996 Cherkassky V, Gehring D, Mulier F. Comparison of adaptive methods for function estimation from samples Ieee Transactions On Neural Networks. 7: 969-984. DOI: 10.1109/72.508939  0.418
1995 Mulier F, Cherkassky V. Self-organization as an iterative kernel smoothing process Neural Computation. 7: 1165-1177. PMID 7584895 DOI: 10.1162/Neco.1995.7.6.1165  0.33
1995 Rooholamini R, Cherkassky V. ATM-Based Multimedia Servers Ieee Multimedia. 2: 39-52. DOI: 10.1109/93.368600  0.315
1992 Cherkassky V, Zhou DN. Comparison of conventional and neural network heuristics for job shop scheduling Proceedings of Spie. 1710: 815-825. DOI: 10.1117/12.140142  0.315
1992 Cherkassky V, Mulier FM. Conventional and neural network approaches to regression Proceedings of Spie. 1709: 840-848. DOI: 10.1117/12.140069  0.384
1992 Cherkassky V, Lari-Naiafi H. Data representation for diagnostic neural networks Ieee Expert-Intelligent Systems and Their Applications. 7: 43-53. DOI: 10.1109/64.163672  0.407
1992 Cherkassky V, Lim EP. Two approaches to knowledge representation and reasoning Ieee Expert-Intelligent Systems and Their Applications. 7: 31-40. DOI: 10.1109/64.153462  0.316
1991 Zhou DN, Cherkassky V, Baldwin TR, Olson DE. A Neural Network Approach to Job-Shop Scheduling Ieee Transactions On Neural Networks. 2: 175-179. DOI: 10.1109/72.80311  0.34
1991 Cherkassky V, Lari-Najafi H. Constrained topological mapping for nonparametric regression analysis Neural Networks. 4: 27-40. DOI: 10.1016/0893-6080(91)90028-4  0.345
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