WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION

摘要

Wavelet, a powerful tool for signal processing, can be used to approximate the target function. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with better sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target funciton with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation experiment show the feasibility and validity of wavelet kernel support vector machines.