The nature of statistical learning theory
The nature of statistical learning theory
IEEE Spectrum
Signal estimation and denoising using VC-theory
Neural Networks
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Data compression and harmonic analysis
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Model complexity control for regression using VC generalization bounds
IEEE Transactions on Neural Networks
Model complexity control and statisticallearning theory
Natural Computing: an international journal
Comparison of model selection for regression
Neural Computation
DWT based beat rate detection in ECG analysis
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
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We present empirical comparisons of several wavelet-denoising methods applied to the problem of removing (denoising) myopotential noise from the observed noisy ECG signal. Namely, we compare the denoising accuracy and robustness of several wavelet thresholding methods (VISU, SURE and soft thresholding) and a new thresholding approach based on Vapink-Chervonenkis (VC) learning theory. Our findings indicate that the VC-based wavelet approach is superior to the standard thresholding methods in that it achieves: Higher denoising accuracy (in terms of both MSE measure and visual quality) and more robust and compact representation of the denoised signal (i.e., it uses fewer wavelets).