4 citations · 6 across the 4 of their papers we have counts for
5 papers
VC Theoretical Explanation of Double Descent
Eng Hock Lee, Vladimir Cherkassky
There has been growing interest in generalization performance of large multilayer neural networks that can be trained to achieve zero training error, while generalizing well on tes…
Single Class Universum-SVM
Sauptik Dhar, Vladimir Cherkassky
This paper extends the idea of Universum learning [1, 2] to single-class learning problems. We propose Single Class Universum-SVM setting that incorporates a priori knowledge (in t…
Multiclass Universum SVM
Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose an analytic span bound for model sele…
Universum Learning for Multiclass SVM
Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky +1
We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose a span bound for MU-SVM that can be u…
Universum Learning for SVM Regression
Sauptik Dhar, Vladimir Cherkassky
This paper extends the idea of Universum learning [18, 19] to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowl…