Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Submodular Optimization for Efficient Semi-supervised Support Vector
  Machines   In this work we present a quadratic programming approximation of the
Semi-Supervised Support Vector Machine (S3VM) problem, namely approximate
QP-S3VM, that can be efficiently solved using off the shelf optimization
packages. We prove that this approximate formulation establishes a relation
between the low density separation and the graph-based models of
semi-supervised learning (SSL) which is important to develop a unifying
framework for semi-supervised learning methods. Furthermore, we propose the
novel idea of representing SSL problems as submodular set functions and use
efficient submodular optimization algorithms to solve them. Using this new idea
we develop a representation of the approximate QP-S3VM as a maximization of a
submodular set function which makes it possible to optimize using efficient
greedy algorithms. We demonstrate that the proposed methods are accurate and
provide significant improvement in time complexity over the state of the art in
the literature.

12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics