Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Vladimir Cherkassky

Showing results (1-10 of 22) with videos related to

Pageof 3
Sort By:
Neural Computation|June 21, 2003
Comparison of model selection for regressionVladimir Cherkassky, Yunqian Ma
IEEE Transactions on Neural Networks|August 27, 2005
Multiple model regression estimationVladimir Cherkassky, Yunqian Ma
IEEE Transactions on Cybernetics|September 30, 2014
Development and evaluation of cost-sensitive universum-SVMSauptik Dhar, Vladimir Cherkassky
Neural Networks : the Official Journal of the International Neural Network Society|May 16, 2009
Another look at statistical learning theory and regularizationVladimir Cherkassky, Yunqian Ma
Neural Networks : the Official Journal of the International Neural Network Society|December 24, 2003
Practical selection of SVM parameters and noise estimation for SVM regressionVladimir Cherkassky, Yunqian Ma
IEEE Transactions on Neural Networks and Learning Systems|May 9, 2014
Generalized SMO algorithm for SVM-based multitask learningFeng Cai, Vladimir Cherkassky
Neural Networks : the Official Journal of the International Neural Network Society|May 11, 2020
Performance metrics for online seizure predictionHsiang-Han Chen, Vladimir Cherkassky
Neural Networks : the Official Journal of the International Neural Network Society|November 1, 2023
To understand double descent, we need to understand VC theoryVladimir Cherkassky, Eng Hock Lee
IEEE Transactions on Neural Networks and Learning Systems|April 26, 2024
Understanding Double Descent Using VC-Theoretical FrameworkEng Hock Lee, Vladimir Cherkassky
Neural Networks : the Official Journal of the International Neural Network Society|July 15, 2009
Predictive learning with structured (grouped) dataLichen Liang, Feng Cai, Vladimir Cherkassky
Pageof 3

Showing results (1-10 of 22) with videos related to

Sort By:
Pageof 3
Neural Computation|June 21, 2003
Comparison of model selection for regressionVladimir Cherkassky, Yunqian Ma
IEEE Transactions on Neural Networks|August 27, 2005
Multiple model regression estimationVladimir Cherkassky, Yunqian Ma
IEEE Transactions on Cybernetics|September 30, 2014
Development and evaluation of cost-sensitive universum-SVMSauptik Dhar, Vladimir Cherkassky
Neural Networks : the Official Journal of the International Neural Network Society|May 16, 2009
Another look at statistical learning theory and regularizationVladimir Cherkassky, Yunqian Ma
Neural Networks : the Official Journal of the International Neural Network Society|December 24, 2003
Practical selection of SVM parameters and noise estimation for SVM regressionVladimir Cherkassky, Yunqian Ma
IEEE Transactions on Neural Networks and Learning Systems|May 9, 2014
Generalized SMO algorithm for SVM-based multitask learningFeng Cai, Vladimir Cherkassky
Neural Networks : the Official Journal of the International Neural Network Society|May 11, 2020
Performance metrics for online seizure predictionHsiang-Han Chen, Vladimir Cherkassky
Neural Networks : the Official Journal of the International Neural Network Society|November 1, 2023
To understand double descent, we need to understand VC theoryVladimir Cherkassky, Eng Hock Lee
IEEE Transactions on Neural Networks and Learning Systems|April 26, 2024
Understanding Double Descent Using VC-Theoretical FrameworkEng Hock Lee, Vladimir Cherkassky
Neural Networks : the Official Journal of the International Neural Network Society|July 15, 2009
Predictive learning with structured (grouped) dataLichen Liang, Feng Cai, Vladimir Cherkassky
Pageof 3