Title: On the Statistical Efficiency of Optimal Kernel Sum Classifiers,
Abstract: 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.