A Novel Smooth Support Vector Regression Based on CHKS Function

Abstract:

Article Preview

This paper presents a new smooth approach to solve support vector regression (SVR). Based on Karush-Kuhn-Tucker complementary condition in optimization theory, a smooth unconstrained optimization model for SVR is built. Since the objective function of the unconstrained SVR model is non-smooth, we apply the smooth techniques and replace the ε-insensitive loss function by CHKS function. Newton-Armijo algorithm is used to solve the smooth CHKS-SSVR model. Primary numerical results illustrate that our proposed approach improves the regression performance and the learning efficiency.

Info:

Periodical:

Edited by:

Ran Chen

Pages:

3746-3751

DOI:

10.4028/www.scientific.net/AMM.44-47.3746

Citation:

Q. Wu "A Novel Smooth Support Vector Regression Based on CHKS Function", Applied Mechanics and Materials, Vols. 44-47, pp. 3746-3751, 2011

Online since:

December 2010

Authors:

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.