文章基本信息

标题：The Asymptotic Study of Smooth Entropy Support Vector Regression
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作者：Guo-sheng Hu ; Jian-hua Zhang
期刊名称：Intelligent Information Management
印刷版ISSN：2150-8194
电子版ISSN：2150-8208
出版年度：2012
卷号：4
期号：3
页码：45-51
DOI：10.4236/iim.2012.43007
出版社：Scientific Research Publishing
摘要：In this paper, a novel formulation, smooth entropy support vector regression (SESVR), is proposed, which is a smooth unconstrained optimization reformulation of the traditional linear programming associated with an ε-insensitive support vector regression. An entropy penalty function is substituted for the plus function to make the objective function con- tinuous ,and a new algorithm involving the Newton-Armijo algorithm proposed to solve the SESVR converge globally to the solution. Theoretically, we give a brief convergence proof to our algorithm. The advantages of our presented algorithm are that we only need to solve a system of linear equations iteratively instead of solving a convex quadratic program, as is the case with a conventional SVR, and lessen the influence of the penalty parameter C in our model. In order to show the efficiency of our algorithm, we employ it to forecast an actual electricity power short-term load. The experimental results show that the presented algorithm, SESVR, plays better precisely and effectively than SVMlight and LIBSVR in stochastic time series forecasting.
关键词：Support Vector Machine; SSVR; Entropy Function; Asymptotic Solution; Forecasting