Abstract
Based on the continuous wavelet transform theory and conditions of the admissible support vector kernel, a novel notion of multidimensional wavelet kernels is proposed for Least Squares Support Vector Machine (LS-WSVM) for pattern recognition and function estimation. Theoretic analysis of the wavelet kernel is discussed in detail. The good approximation property of wavelet kernel function enhances the generalization ability of LS-WSVM method and some experimental results are presented to illustrate the effectiveness and feasibility of the proposed method.
This work was supported by the national 973 key fundamental research project of China under grant 2002CB312200 and national 863 high technology projects foundation of China under grant 2002AA412010.
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Wen, X., Cai, Y., Xu, X. (2005). Least Squares Support Vector Machine Based on Continuous Wavelet Kernel. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_135
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DOI: https://doi.org/10.1007/11427391_135
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