Learning graphical models for hypothesis testing and classification

Learning graphical models for hypothesis testing and classification

Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques to exploit this modeling ability for binary classification by discriminatively learning such models from labeled traini...

Full description

Bibliographic Details
Main Authors: Tan, Vincent Yan Fu (Contributor), Sanghavi, Sujay (Author), Fisher, John W., III (Contributor), Willsky, Alan S. (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor), Massachusetts Institute of Technology. Stochastic Systems Group (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2012-10-04T16:50:38Z.
Subjects:
Article
Online Access:Get fulltext

Internet

Get fulltext

Similar Items