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Multiwavelet Support Vector Machine and its Applications

G. Y. Chen, W.F. Xie


Support Vector Machines (SVM) have been developed by Vapnik and are gaining popularity because of their attractive features and promising performance in function regression and pattern classification. Recently, SVM with the scalar wavelet kernel has been developed and it has exhibited better performance in function regression and pattern recognition. In this paper, SVM with multiwavelet kernels is studied and applied to signal regression and pattern recognition. The motivation to use multiwavelets is because multiwavelets have better properties than scalar wavelets. Experiments show that this method is better than the existing SVM for function regression and pattern recognition.


Multiwavelets, signal regression, support vector machine, pattern recognition

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