IIM  Vol.4 No.3 , May 2012
The Asymptotic Study of Smooth Entropy Support Vector Regression
ABSTRACT
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.

Cite this paper
G. Hu and J. Zhang, "The Asymptotic Study of Smooth Entropy Support Vector Regression," Intelligent Information Management, Vol. 4 No. 3, 2012, pp. 45-51. doi: 10.4236/iim.2012.43007.
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