11th Joint International Conference on Information Sciences

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

Authors
Xiaoming Xu 0, Ning Ye, Qiaolin Ye
Corresponding Author
Xiaoming Xu
0Nanjing University of Aeronautics & Astronautics,Nanjing
Available Online December 2008.
DOI
https://doi.org/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.
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Proceedings
11th Joint International Conference on Information Sciences
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2008
ISBN
978-90-78677-18-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2008.106How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 11th Joint International Conference on Information Sciences
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.106
DO  - https://doi.org/10.2991/jcis.2008.106
ID  - Xu2008/12
ER  -