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 ...

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 ...

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 ...

... {Selecting a Reduced Set for Building Sparse Support Vector Regression in the Primal}, optcrossref = {}, optkey = {}, booktitle = {The 11th Pacific-Asia ...

Liefeng Bo, Ling Wang, Licheng Jiao: Selecting a Reduced Set for Building Sparse Support Vector Regression in the Primal. PAKDD 2007: 35-46.

SVM sparsity is significant because it requires a low economy and produces high efficiency for applications. Hence, it is necessary to improve SVM sparsity.

Selecting a Reduced Set for Building Sparse Support Vector Regression in the Primal. record by Licheng Jiao • Selecting a Reduced Set for Building Sparse ...

Liefeng Bo, Ling Wang, and Licheng Jiao, Selecting a Reduced Set for Building Sparse Support Vector Regression in the Primal, In The Eleventh Pacific-Asia ...

Selecting a Reduced Set for Building Sparse Support Vector Regression in the Primal · Liefeng BoLing WangL. Jiao. Computer Science, Mathematics. PAKDD. 2007.

A non-convex loss function is proposed to improve the robustness of SVR to outliers and gain better generalization performance than the classical SVR with ...