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Proceedings Paper

Online independent Lagrangian support vector machine
Author(s): Yu Jin; Hongbing Ji; Lei Wang; Lin Lin
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Paper Abstract

In this paper, a novel incremental learning method called online independent Lagrangian support vector machine (OILSVM) is proposed. It achieves comparable classification accuracy with benchmark Lagrangian support vector machine (LSVM), while still enjoying the time efficiency of online learning machines. As opposed to the newly proposed OLSVM that utilizes the KKT conditions as data selection strategy, the size of the solution obtained by OILSVM using a linear independence check is always bounded, which implies bounded memory requirements, training and testing time. Experimental results demonstrate the effectiveness of the proposed OILSVM.

Paper Details

Date Published: 2 December 2011
PDF: 8 pages
Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80041H (2 December 2011); doi: 10.1117/12.903030
Show Author Affiliations
Yu Jin, Xidian Univ. (China)
Hongbing Ji, Xidian Univ. (China)
Lei Wang, Xidian Univ. (China)
Lin Lin, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 8004:
MIPPR 2011: Pattern Recognition and Computer Vision
Jonathan Roberts; Jie Ma, Editor(s)

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