Density Weighted Least Squares Support Vector Machine
Least squares support vector machine is works with a sum squares errors cost function which used to minimization its empirical risk.The higher distribution samples are well fitted by the model because the estimation of the support values is optimal in the case of a Gaussian distribution,but the peak samples are poor fitted for its sparse distribution.A density weighted least squares support vector machine is proposed here,which based on the weighted least squares method.In this model,the errors of sparsely distributing samples are higher weighted in the optimization function,which help to improve the fitting accuracy of peak samples significantly with the average accuracy maintained simultaneously.The feasibility and the efficacy of this model are demonstrated on function fitting and load forecast of power system in the last.
XU Shuqiong YUAN Conggui ZHANG Xinzheng
Automation Department,Guangdong University of Technology,Guangzhou 511442,P.R.China Guangzhou Vocational College of Technology &Business,Guangzhou 511442,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-5
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）