PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Parallel decomposition approaches for training support vector machines
Thomas Serafini, Gaetano Zanghirati and Luca Zanni
In: Parallel Computing: Software Technology, Algorithms, Architectures and Applications Advances in Parallel Computing (13). (2004) Elsevier B.V. , Amsterdam, The Netherlands , pp. 259-266. ISBN 0-444-51689-1

Abstract

We consider parallel decomposition techniques for solving the large quadratic programming (QP) problems arising in training support vector machines. A recent technique is improved by introducing an efficient solver for the inner QP subproblems and a preprocessing step useful to hot start the decomposition strategy. The effectiveness of the proposed improvements is evaluated by solving large-scale benchmark problems on different parallel architectures.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3372
Deposited By:Gaetano Zanghirati
Deposited On:09 February 2008