Improved support vector regression algorithm combining with probability distribution and monotone property
ZHANG Qing
YAN Xue-feng
Abstract：The traditional support vector regression (SVR) is only based on data information, and a great deal of prior knowledge is neglected. In order to improve its performance, a new SVR algorithm combining with probability distribution and monotone property is proposed. Firstly, the dual quadratic programming problem is simplified as a linear one. Secondly, the monotonic constraints associated with Lagrange multiplier are added. Thirdly, the particle swarm optimization (PSO) is employed to optimize the penalty and kernel parameters. And the fitness function of PSO is the deviation of the probability distribution estimated by four-order moments. The experiment results show that the performance of the proposed SVR model is improved and the developed model satisfies probability distribution and monotone property.
Keywords：support vector regressionprobability distributionmonotone propertyparticle swarm optimization
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 671-676 )