Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimality Implies Kernel Sum Classifiers are Statistically Efficient   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.

31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom