Title

A Fast Revised Simplex Method For Svm Training

Abstract

Active set methods for training the Support Vector Machines (SVM) are advantageous since they enable incremental training and, as we show in this research, do not exhibit exponentially increasing training times commonly associated with the decomposition methods as the SVM training parameter, C, is increased or the classification difficulty increases. Previous implementations of the active set method must contend with singularities, especially associated with the linear kernel, and must compute infinite descent directions, which may be inefficient, especially as C is increased. In this research, we propose a revised simplex method for quadratic programming, which has a guarantee of non-singularity for the sub-problem, and show how this can be adapted to SVM training. © 2008 IEEE.

Publication Date

1-1-2008

Publication Title

Proceedings - International Conference on Pattern Recognition

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/icpr.2008.4761810

Socpus ID

77957949177 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77957949177

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