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 / Serafini, Thomas; G., Zanghirati; Zanni, Luca. - STAMPA. - 13:(2004), pp. 259-266. (Intervento presentato al convegno International Conference on Parallel Computing (ParCo2003) tenutosi a Tech Univ Dresden, Ctr High Performance Comp, Dresden, GERMANY nel SEP 02-05, 2003) [10.1016/S0927-5452(04)80035-2].
Parallel decomposition approaches for training support vector machines
SERAFINI, Thomas;ZANNI, Luca
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.
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