Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training very large scale nonlinear SVMs using Alternating Direction
  Method of Multipliers coupled with the Hierarchically Semi-Separable kernel
  approximations   Typically, nonlinear Support Vector Machines (SVMs) produce significantly
higher classification quality when compared to linear ones but, at the same
time, their computational complexity is prohibitive for large-scale datasets:
this drawback is essentially related to the necessity to store and manipulate
large, dense and unstructured kernel matrices. Despite the fact that at the
core of training a SVM there is a \textit{simple} convex optimization problem,
the presence of kernel matrices is responsible for dramatic performance
reduction, making SVMs unworkably slow for large problems. Aiming to an
efficient solution of large-scale nonlinear SVM problems, we propose the use of
the \textit{Alternating Direction Method of Multipliers} coupled with
\textit{Hierarchically Semi-Separable} (HSS) kernel approximations. As shown in
this work, the detailed analysis of the interaction among their algorithmic
components unveils a particularly efficient framework and indeed, the presented
experimental results demonstrate a significant speed-up when compared to the
\textit{state-of-the-art} nonlinear SVM libraries (without significantly
affecting the classification accuracy).

15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg