Nonlinear Proximal Support Vector Machine Classifiers Aiming At Large Scale Classification Problems
Xiaoming Xu 0, Ning Ye, Qiaolin Ye
0Nanjing University of Aeronautics & Astronautics,Nanjing
Available Online December 2008.
- https://doi.org/10.2991/jcis.2008.106How to use a DOI?
- large scale classification problems; inversion; conjugate gradient method
- In , 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.
- 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 -