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Some Aspects of Learning Rates for SVMs

Some Aspects of Learning Rates for SVMs

This video was recorded at Machine Learning Summer School (MLSS), Chicago 2005. We present some learning rates for support vector machine classification. In particular we discuss a recently proposed geometric noise assumption which allows to bound the approximation error for Gaussian RKHSs. Furthermore we show how a noise assumption proposed by Tsybakov can be used to obtain learning rates between 1/sqrt(n) and 1/n. Finally, we describe the influence of the approximation error on the overall learning rate.
Keywords:
videolectures, ocwc, oec
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More about this material

Material Type:
Presentation
Date Added to MERLOT:
February 10, 2015
Date Modified in MERLOT:
February 10, 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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