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Limitations of kernel and multiple kernel learning

Limitations of kernel and multiple kernel learning

This video was recorded at 1st International Workshop on Similarity-Based Pattern Analysis and Recognition. Many low Vapnik-Chervonenkis (and hence statistically learnable) classes cannot be represented as linear classes in such a way that they can be learnt with large margin approaches. We review these results and then consider recent bounds for multiple kernel learning that suggest large margin methods may be more general applicable.... Show More
Keywords:
videolectures, ocwc, oec
Disciplines:
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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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