In this paper, we propose three important enhancements of the approximate cutting plane algorithm (CPA) to train Support Vector Machines with structural kernels: (i) we exploit a compact yet exact representation of cutting plane models using directed acyclic graphs to speed up both training and classification, (ii) we provide a parallel implementation, which makes the training scale almost linearly with the number of CPUs, and (iii) we propose an alternative sampling strategy to handle class-imbalanced problem and show that theoretical convergence bounds are preserved. The experimental evaluations on three diverse datasets demonstrate the soundness of our approach and the possibility to carry out fast learning and classification with structural kernels.

Fast support vector machines for structural kernels

Severyn, Aliaksei;Moschitti, Alessandro
2011-01-01

Abstract

In this paper, we propose three important enhancements of the approximate cutting plane algorithm (CPA) to train Support Vector Machines with structural kernels: (i) we exploit a compact yet exact representation of cutting plane models using directed acyclic graphs to speed up both training and classification, (ii) we provide a parallel implementation, which makes the training scale almost linearly with the number of CPUs, and (iii) we propose an alternative sampling strategy to handle class-imbalanced problem and show that theoretical convergence bounds are preserved. The experimental evaluations on three diverse datasets demonstrate the soundness of our approach and the possibility to carry out fast learning and classification with structural kernels.
2011
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases
Verlag Berlin, Heidelberg
Springer
978-3-642-23807-9
Severyn, Aliaksei; Moschitti, Alessandro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/89071
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