A New Support Vector Machine for Data Mining

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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.

Haoran Zhang, Xiaodong Wang, Changjiang Zhang, Xiu

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ADMA 2005 | Data Mining | Dual Optimal Formation | Standard Support Vector | Support Vector Machine |

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Added	26 Jun 2010
Updated	26 Jun 2010
Type	Conference
Year	2005
Where	ADMA
Authors	Haoran Zhang, Xiaodong Wang, Changjiang Zhang, Xiuling Xu

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A New Support Vector Machine for Data Mining

ADMA 2005 | Data Mining | Dual Optimal Formation | Standard Support Vector | Support Vector Machine |

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