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We propose a novel combination of optimization tools with learning theorybounds in order to analyze the sample complexity of optimal kernel sumclassifiers. This contrasts the typical learning theoretic results which holdfor all (potentially suboptimal) classifiers. Our work also justifiesassumptions made in prior work on multiple kernel learning. As a byproduct ofour analysis, we also provide a new form of Rademacher complexity forhypothesis classes containing only optimal classifiers.

Optimality Implies Kernel Sum Classifiers are Statistically Efficient