Journal
NEURAL NETWORKS
Volume 57, Issue -, Pages 1-11Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2014.05.003
Keywords
Support vector regression; Noise model; Loss function; Inequality constraints; Wind speed forecasting
Funding
- National Program on Key Basic Research Project (973 Program) [2012CB215201]
- National Natural Science Foundation of China (NSFC) [61222210, 61170107, 61105054, 61300121]
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Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of v-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique. (C) 2014 Elsevier Ltd. All rights reserved.
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