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A new function approximation algorithm based on twin support vector machines (TSVM) is presented in this paper. Support vector regression (SVR) has been shown to have good robust properties against noise in function approximation, however, the overfitting phenomena cannot be eliminated if the parameters used in SVR are improperly selected, and the selection of various parameters is not straightforward...
In order to improve the performance of a support vector regression, a new method for modified kernel function is proposed. In this method the information of whole samples is included in kernel function by conformal mapping. So the Kernel function is data-dependent. With random initial parameter of kernel function, iterative modifying is not stopped until satisfactory effect. Comparing with the conventional...
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