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...
| Main Authors: | , , , |
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| Other Authors: | , , , |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2012-10-04T16:50:38Z.
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| Subjects: | |
| Online Access: | Get fulltext |