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SUPPORT VECTOR MACHINES BASED ON THE ORTHOGONAL PROJECTION KERNEL OF FATHER WAVELET

LI ZHANG

Institute of Intelligent Information Processing, Xidian University, Xi'an, 710071, China

Institute of Automation, Shanghai Jiao Tong University, Shanghai 200030, China

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WEIDA ZHOU

Institute of Intelligent Information Processing, Xidian University, Xi'an, 710071, China

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LICHENG JIAO

Institute of Intelligent Information Processing, Xidian University, Xi'an, 710071, China

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https://doi.org/10.1142/S1469026805001489Cited by:11 (Source: Crossref)

Abstract

Recently the study on the theory of wavelets shows that the wavelets have not only the multi-resolution property both in frequency and time domain, but also the good approximation ability. SVMs based on the statistical learning theory are a kind of general and effective learning machines, and have described for us the nice application blueprint in machine learning domain. There exists a bottleneck problem, or the pre-selection of kernel parameter for SVMs. In this paper, the orthogonal projection kernels of father wavelet (OPFW kernels) are introduced into SVMs. In doing so SVMs based on the OPFW kernels can have good performance in both approximation and generalisation. Simultaneously the parameter pre-selection of wavelet kernels can be implemented by discrete wavelet transform. Experiments on regression estimation illustrate the approximation and generalisation ability of our method.

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