Abstract
The min-max modular support vector machine (M3-SVM) was proposed for dealing with large-scale pattern classification problems. M3-SVM divides training data to several sub-sets, and combine them to a series of independent sub-problems, which can be learned in a parallel way. In this paper, we explore the use of the geometric relation among training data in task decomposition. The experimental results show that the proposed task decomposition method leads to faster training and better generalization accuracy than random task decomposition and traditional SVMs.
This work was supported in part by the National Natural Science Foundation of China via the grants NSFC 60375022 and NSFC 60473040, as well as Open Fund of Grid Computing Center, Shanghai Jiao Tong University.
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Wang, K., Zhao, H., Lu, B. (2005). Task Decomposition Using Geometric Relation for Min-Max Modular SVMs. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_142
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DOI: https://doi.org/10.1007/11427391_142
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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