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Sparse support vector regression (SpSVR) method is proposed to improve the leaning speed without decreasing generalization performance. Firstly, the primal problem of support vector regression is directly optimized through Newton optimization method. Then, in order to realize the sparseness of SVR, Cholesky decomposition is used to update the Hessian matrix in SVR primal problem. Finally, such proposed...
Due to time series forecasting involves a rather complex data pattern, there are lots of novel forecasting approaches to improve the forecasting accuracy. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, SVM applies the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the...
In the competition paradigm of the electric power markets, both power producers and consumers need some price prediction tools in order to plan their bidding strategies. This paper studies the problem of modeling market clearing price forecasting in deregulated markets. And electricity price forecasting with support vector machines based on artificial fish swarm algorithm is provided. Except considering...
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