Limitations of kernel and multiple kernel learning

author: John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
published: Oct. 17, 2011,   recorded: September 2011,   views: 479
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Description

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.

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