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Vicinal Risk Minimization Based Probability Density Function Estimation Algorithm Using SVM

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2 Author(s)
Luan Hai-Yan ; China Nat. Digital Switching Syst. Eng. & Technol. Res. Center, Zhengzhou, China ; Jiang Hua

Many statistic based machine learning methods depend on the estimation of probability density function from observations. Non-parametric density estimation algorithms based on minimizing expirical risk using support vector machine (SVM) are quite general and powerful, but have a significant disadvantage in the smoothness of estimation result. In this paper, we studies the vicinal risk minimization based estimation algorithm, and propose a new construction algorithm of vicinity function. Experiments are carried out which prove that the performance of new algorithm is obviously improved.

Published in:

Information and Computing (ICIC), 2010 Third International Conference on  (Volume:4 )

Date of Conference:

4-6 June 2010