This paper proposes a new support vector machine (SVM) with a robust loss function for data mining. Its dual optimal formation is also constructed. A gradient based algorithm is designed for fast and simple implementation of the new support vector machine. At the same time it analyzes algorithm's convergence condition and gives a formula to select learning step size. Numerical simulation results show that the new support vector machine performs significantly better than a standard support vector machine. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Zhang, H., Wang, X., Zhang, C., & Xu, X. (2005). A new support vector machine for data mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3584 LNAI, pp. 256–266). Springer Verlag. https://doi.org/10.1007/11527503_31
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