NDLI: Density weighted least squares support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Xu Shuqiong Yuan Conggui Zhang Xinzheng
Copyright Year	2011
Description	Author affiliation: Automation Department, Guangdong University of Technology, Guangzhou 511442, China (Xu Shuqiong; Yuan Conggui; Zhang Xinzheng)
Abstract	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.
Starting Page	5310
Ending Page	5314
File Size	230012
Page Count	5
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Support vector machines Atmospheric modeling Weighted Least Squares Fitting Estimation Forecast Kernel Density Estimator Electronic mail Support Vector Machine Least squares approximation Artificial intelligence
Content Type	Text
Resource Type	Article

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