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.

Parallel decomposition approaches for training support vector machines

ZANGHIRATI, Gaetano;
2004

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.
2004
9780444516893
support vector machines; large-scale; decomposition; quadratic programming
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1211191
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