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Poster
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Raphael Meyer · Jean Honorio
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.
Author Information
Raphael Meyer (Purdue University)
Graduating with BS from Purdue University, Spring 2019. Joining NYU Tandon, Fall 2019.
Jean Honorio (Purdue University)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: Optimality Implies Kernel Sum Classifiers are Statistically Efficient »
Tue. Jun 11th 11:35 -- 11:40 PM Room Room 103
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