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

Chih-Jen Lin

Neural computation

Showing results (1-10 of 9) with videos related to

Pageof 1
Sort By:
Neural Computation|August 25, 2007
Projected gradient methods for nonnegative matrix factorizationChih-Jen Lin
Neural Computation|August 16, 2002
Training nu-support vector regression: theory and algorithmsChih-Chung Chang, Chih-Jen Lin
Neural Computation|February 1, 2014
Large-scale linear rankSVMChing-Pei Lee, Chih-Jen Lin
Neural Computation|March 9, 2013
A study on L2-loss (squared hinge-loss) multiclass SVMChing-Pei Lee, Chih-Jen Lin
Neural Computation|June 21, 2003
Asymptotic behaviors of support vector machines with Gaussian kernelS Sathiya Keerthi, Chih-Jen Lin
Neural Computation|June 17, 2015
Subsampled Hessian Newton Methods for Supervised LearningChien-Chih Wang, Chun-Heng Huang, Chih-Jen Lin
Neural Computation|May 22, 2002
A note on the decomposition methods for support vector regressionShuo-Peng Liao, Hsuan-Tien Lin, Chih-Jen Lin
Neural Computation|October 28, 2003
Radius margin bounds for support vector machines with the RBF kernelKai-Min Chung, Wei-Chun Kao, Chia-Liang Sun, et al.
Neural Computation|April 14, 2018
Distributed Newton Methods for Deep Neural NetworksChien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Neural Computation|August 25, 2007
Projected gradient methods for nonnegative matrix factorizationChih-Jen Lin
Neural Computation|August 16, 2002
Training nu-support vector regression: theory and algorithmsChih-Chung Chang, Chih-Jen Lin
Neural Computation|February 1, 2014
Large-scale linear rankSVMChing-Pei Lee, Chih-Jen Lin
Neural Computation|March 9, 2013
A study on L2-loss (squared hinge-loss) multiclass SVMChing-Pei Lee, Chih-Jen Lin
Neural Computation|June 21, 2003
Asymptotic behaviors of support vector machines with Gaussian kernelS Sathiya Keerthi, Chih-Jen Lin
Neural Computation|June 17, 2015
Subsampled Hessian Newton Methods for Supervised LearningChien-Chih Wang, Chun-Heng Huang, Chih-Jen Lin
Neural Computation|May 22, 2002
A note on the decomposition methods for support vector regressionShuo-Peng Liao, Hsuan-Tien Lin, Chih-Jen Lin
Neural Computation|October 28, 2003
Radius margin bounds for support vector machines with the RBF kernelKai-Min Chung, Wei-Chun Kao, Chia-Liang Sun, et al.
Neural Computation|April 14, 2018
Distributed Newton Methods for Deep Neural NetworksChien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, et al.
Pageof 1