电子科技Issue(9)：40-43,4.
一种基于改进W-SVM算法的概率密度估计
An Improved Probability W-SVM Algorithm Based on Density Estimates
摘要
Abstract
The probability density estimation algorithm based on W-SVM is studied.The existing algorithms only consider either the sampling time or the sample density , resulting in lager error probability density results .An improved W-SVM algorithm is proposed to improve the estimation precision by considering both the sample point of time and the use of area .Different types of penalty weighting coefficients are selected and normalized , and the most appropriate weighting factor is found by the grid optimization method .The simulation results show that the mean square error by the proposed improved probability weighted support vector density estimation is much smaller than that by traditional algorithms .关键词
加权支持向量机/概率密度估计/加权系数/归一化Key words
W-SVM/probability density estimation/weighting factor/normalized分类
信息技术与安全科学引用本文复制引用
曹华孝,王成,邵秀娟,龚凌..一种基于改进W-SVM算法的概率密度估计[J].电子科技,2014,(9):40-43,4.基金项目
四川师范大学成都学院国家级大学生创新训练基金资助项目 （）
