Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28946
Title: The forecasting model based on modified SVRM and PSO penalizing Gaussian noise
Authors: Wu, Q
Law, R 
Keywords: Support vector machine
Gaussian loss function
Particle swarm optimization
Adaptive mutation
Forecasting
Issue Date: 2011
Publisher: Pergamon Press
Source: Expert systems with applications, 2011, v. 38, no. 3, p. 1887-1894 How to cite?
Journal: Expert systems with applications 
Abstract: The epsilon-insensitive loss function has no penalizing capability for white (Gaussian) noise from training series in support vector regression machine (SVRM). To overcome the disadvantage, the relation between Gaussian noise model and loss function of SVRM is studied. And then, a new loss function is proposed to penalize the Gaussian noise in this paper. Based on the proposed loss function, a new nu-SVRM, which is called g-SVRM, is put forward to deal with training set. To seek the optimal parameters of g-SVRM, an improved particle swarm optimization is also proposed. The results of application in car sale forecasts show that the forecasting approach based on the g-SVRM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than nu-SVRM and other traditional methods.
URI: http://hdl.handle.net/10397/28946
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2010.07.120
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