Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Wavelets and subband coding
The nature of statistical learning theory
The nature of statistical learning theory
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
AI Game Programming Wisdom
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Hybrid wavelet-support vector classification of waveforms
Journal of Computational and Applied Mathematics
Feature extraction by shape-adapted local discriminant bases
Signal Processing
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Theory and design of signal-adapted FIR paraunitary filter banks
IEEE Transactions on Signal Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Pattern Recognition
Expert Systems with Applications: An International Journal
Learning with infinitely many features
Machine Learning
Wavelet adaptation for automatic voice disorders sorting
Computers in Biology and Medicine
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Hybrid wavelet-large margin classifiers have recently proven to solve difficult signal classification problems in cases where solely using a large margin classifier like, e.g., the Support Vector Machine may fail. In this paper, we evaluate several criteria rating feature sets obtained from various orthogonal filter banks for the classification by a Support Vector Machine. Appropriate criteria may then be used for adapting the wavelet filter with respect to the subsequent support vector classification. Our results show that criteria which are computationally more efficient than the radius-margin Support Vector Machine error bound are sufficient for our filter adaptation and, hence, feature selection. Further, we propose an adaptive search algorithm that, once the criterion is fixed, efficiently finds the optimal wavelet filter. As an interesting byproduct we prove a theorem which allows the computation of the radius of a set of vectors by a standard Support Vector Machine.