Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Confidence Sets using Support Vector Machines   The goal of confidence-set learning in the binary classification setting is
to construct two sets, each with a specific probability guarantee to cover a
class. An observation outside the overlap of the two sets is deemed to be from
one of the two classes, while the overlap is an ambiguity region which could
belong to either class. Instead of plug-in approaches, we propose a support
vector classifier to construct confidence sets in a flexible manner.
Theoretically, we show that the proposed learner can control the non-coverage
rates and minimize the ambiguity with high probability. Efficient algorithms
are developed and numerical studies illustrate the effectiveness of the
proposed method.

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The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg