Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Guaranteed Classification via Regularized Similarity Learning   Learning an appropriate (dis)similarity function from the available data is a
central problem in machine learning, since the success of many machine learning
algorithms critically depends on the choice of a similarity function to compare
examples. Despite many approaches for similarity metric learning have been
proposed, there is little theoretical study on the links between similarity
met- ric learning and the classification performance of the result classifier.
In this paper, we propose a regularized similarity learning formulation
associated with general matrix-norms, and establish their generalization
bounds. We show that the generalization error of the resulting linear separator
can be bounded by the derived generalization bound of similarity learning. This
shows that a good gen- eralization of the learnt similarity function guarantees
a good classification of the resulting linear classifier. Our results extend
and improve those obtained by Bellet at al. [3]. Due to the techniques
dependent on the notion of uniform stability [6], the bound obtained there
holds true only for the Frobenius matrix- norm regularization. Our techniques
using the Rademacher complexity [5] and its related Khinchin-type inequality
enable us to establish bounds for regularized similarity learning formulations
associated with general matrix-norms including sparse L 1 -norm and mixed
(2,1)-norm.

6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an