A flexible support vector machine for regression

作者：Chen Xiaobo^*; Yang Jian; Liang Jun

来源：Neural Computing & Applications, 2012, 21(8): 2005-2013.

DOI：10.1007/s00521-011-0623-5

• Support vector machine
• Function approximation
• epsilon-Insensitive margin
• Parametric insensitive loss function

In this paper, a novel regression algorithm coined flexible support vector regression is proposed. We first model the insensitive zone in classic support vector regression, respectively, by its up- and down-bound functions and then give a kind of generalized parametric insensitive loss GPILF). Subsequently, based on GPILF, we propose an optimization criterion such that the unknown regressor and its up- and down-bound functions can be found simultaneously by solving a single quadratic programming problem. Experimental results on both several publicly available benchmark data sets and time series prediction show the feasibility and effectiveness of the proposed method.

• 出版日期2012-11
• 单位江苏大学; 南京大学