Nonlinear Proximal Support Vector Machine Classifiers Aiming At Large Scale Classification Problems

DOI

10.2991/jcis.2008.106How to use a DOI?

Keywords

large scale classification problems; inversion; conjugate gradient method

Abstract

In [1], Fung et al, had constructed by a very fast algorithm: PSVM classifier, which mainly makes use of the Sherman-Morrison-Woodbury (SWM) identity [1, 7, 8]. However, for one thing, when handling nonlinear problems, the matrix in (1) always is of dimension , such that the SWM identity is of no use. For another, for large scale classification problems, its inversion is not feasible and it is not stored. Aiming at the orientation problems, proposed in this paper is new fast algorithm. Experimental results also show LPSVM is fast and feasible to solve large scale classification problems.

Copyright

© 2008, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoming Xu
AU  - Ning Ye
AU  - Qiaolin Ye
PY  - 2008/12
DA  - 2008/12
TI  - Nonlinear Proximal Support Vector Machine Classifiers Aiming At Large Scale Classification Problems
BT  - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)
PB  - Atlantis Press
SP  - 627
EP  - 633
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.106
DO  - 10.2991/jcis.2008.106
ID  - Xu2008/12
ER  -