Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhang, H., Wang, X., Zhang, C., Xu, X. (2005). A New Support Vector Machine for Data Mining. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_31
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DOI: https://doi.org/10.1007/11527503_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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