Selecting a Reduced Set for Building Sparse Support Vector Regression in the Primal

Recent work shows that Support vector machines (SVMs) can be solved efficiently in the primal. This paper follows this line of research and shows how to build sparse support vector regression (SVR) in the primal, thus providing for us scalable, sparse support vector regression algorithm, named SSVR-SRS. Empirical comparisons show that the number of basis functions required by the proposed algorithm to achieve the accuracy close to that of SVR is far less than the number of support vectors of SVR.

• Type: Conference
• Year: 2007
• Where: PAKDD
• Authors: Liefeng Bo, Ling Wang, Licheng Jiao
• Keywords: Data Mining, PAKDD 2007, Sparse Support Vector, Support Vector Regression, Support Vectors