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PAC-Bayesian Learning of Linear Classifiers

PAC-Bayesian Learning of Linear Classifiers

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. We present a general PAC-Bayes theorem from which all known PAC-Bayes bounds are simply obtained as particular cases. We also propose different learning algorithms for finding linear classifiers that minimize these PAC-Bayes risk bounds. These learning algorithms are generally competitive with both AdaBoost and the SVM.... Show More
Keywords:
videolectures, ocwc, oec
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More about this material

Material Type:
Presentation
Date Added to MERLOT:
February 8, 2015
Date Modified in MERLOT:
February 8, 2015
Author:
Submitter:
The Open Education Consortium
Primary Audience:
College General Ed, College Lower Division, College Upper Division
Technical Format:
Video
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Accessibility Information Available:
Unknown
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States

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