Online independent Lagrangian support vector machine

Yu Jin; Hongbing Ji; Lei Wang; Lin Lin

doi:10.1117/12.903030

2 December 2011 Online independent Lagrangian support vector machine

Yu Jin, Hongbing Ji, Lei Wang, Lin Lin

Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 80041H (2011) https://doi.org/10.1117/12.903030
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

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.

Citation Download Citation

Yu Jin, Hongbing Ji, Lei Wang, and Lin Lin "Online independent Lagrangian support vector machine", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80041H (2 December 2011); https://doi.org/10.1117/12.903030

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