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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.