Abstract
In order to handle large-scale pattern classification problems, various sequential and parallel classification methods have been developed according to the divide-and-conquer principle. However, existing sequential methods need long training time, and some of parallel methods lead to generalization accuracy decreasing and the number of support vectors increasing. In this paper, we propose a novel hierarchical and parallel method for training support vector machines. The simulation results indicate that our method can not only speed up training but also reduce the number of support vectors while maintaining the generalization accuracy.
This work was supported by the National Natural Science Foundation of China under the grants NSFC 60375022 and NSFC 60473040. This work was also supported in part by Open Fund of Grid Computing Center, Shanghai Jiao Tong University.
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Wen, Y., Lu, B. (2005). A Hierarchical and Parallel Method for Training Support Vector Machines. 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_141
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DOI: https://doi.org/10.1007/11427391_141
Publisher Name: Springer, Berlin, Heidelberg
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