Ten lectures on wavelets
The nature of statistical learning theory
The nature of statistical learning theory
On global, local, mixed and neighborhood kernels for support vector machines
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Robust Linear and Support Vector Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
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In this paper a multiresolution wavelet kernel function (MWKF) is proposed for support vector regression. It is different from traditional SVR that the process of reducing dimension is utilized before increasing dimension. The nonlinear mapping ${\it \Phi}(x)$ from the input space S to the feature space has explicit expression based on dimensionality reduction and wavelet multiresolution analysis. This wavelet kernel function can be represented by inner product. This method guarantee that quadratic program of support vector regression has feasible solution and need not parameter selecting in kernel function. Numerical experiments demonstrate the effectiveness of this method.