An application of the learning theory to wavelet based signal denoising

In this paper the statistical learning theory is applied to signal denoising using wavelets. The methodology is based on the estimation of the functional relationship between the Vapnik-Chervonenkis (VC) dimension and approximation complexity. Experimental results confirm the basic assumptions.