DocumentCode :
2541361
Title :
Sparse support vector regression algorithm with piecewise loss function
Author :
Hu, Gensheng
Author_Institution :
Dept. of Comput. Sci., Shangqiu Normal Coll., Shangqiu, China
fYear :
2010
fDate :
16-18 April 2010
Firstpage :
138
Lastpage :
141
Abstract :
Applying sparse algorithm can improve the prediction speed of support vector regression effectively. This paper solves sparse support vector regression with piecewise loss function based on iterative reweight method. By reducing support vector number, the length of regression function expansion and the prediction time of regression function for new samples are decreased. Comparing with sparse LS-SVR, the method has the advantages of suiting different noise data-, steady prediction performance and good generalization performance.
Keywords :
iterative methods; regression analysis; support vector machines; iterative reweight method; piecewise loss function; regression function expansion; sparse support vector regression; support vector number; Constraint optimization; Educational institutions; Iterative algorithms; Iterative methods; Kernel; Lagrangian functions; Large-scale systems; Least squares methods; Quadratic programming; Support vector machines; Iterative Reweight Method; sparse algorithm; support vector regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5263-7
Electronic_ISBN :
978-1-4244-5265-1
Type :
conf
DOI :
10.1109/ICIME.2010.5477495
Filename :
5477495
Link To Document :
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