Paper Title:
A Novel Smooth Support Vector Regression Based on CHKS Function
  Abstract

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
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