Poster
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Raphael Meyer · Jean Honorio
Pacific Ballroom #204
Keywords: [ Supervised Learning ] [ Statistical Learning Theory ]
We propose a novel combination of optimization tools with learning theory bounds in order to analyze the sample complexity of optimal kernel sum classifiers. This contrasts the typical learning theoretic results which hold for all (potentially suboptimal) classifiers. Our work also justifies assumptions made in prior work on multiple kernel learning. As a byproduct of our analysis, we also provide a new form of Rademacher complexity for hypothesis classes containing only optimal classifiers.
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