Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Ten lectures on wavelets
Multirate systems and filter banks
Multirate systems and filter banks
Wavelets and subband coding
The nature of statistical learning theory
The nature of statistical learning theory
Multistep scattered data interpolation using compactly supported radial basis functions
Journal of Computational and Applied Mathematics - Special issue on scattered data
Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Geometry and invariance in kernel based methods
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IEEE Transactions on Signal Processing
Frame-theoretic analysis of oversampled filter banks
IEEE Transactions on Signal Processing
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Feature extraction by shape-adapted local discriminant bases
Signal Processing
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Efficient wavelet adaptation for hybrid wavelet-large margin classifiers
Pattern Recognition
Pattern Recognition
Artificial Intelligence in Medicine
| Hi-index | 7.29 |
The support vector machine (SVM) represents a new and very promising technique for machine learning tasks involving classification, regression or novelty detection. Improvements of its generalization ability can be achieved by incorporating prior knowledge of the task at hand.We propose a new hybrid algorithm consisting of signal-adapted wavelet decompositions and hard margin SVMs for waveform classification. The adaptation of the wavelet decompositions is tailored for hard margin SV classifiers with radial basis functions as kernels. It allows the optimization of the representation of the data before training the SVM and does not suffer from computationally expensive validation techniques.We assess the performance of our algorithm against the background of current concerns in medical diagnostics, namely the classification of endocardial electrograms and the detection of otoacoustic emissions. Here the performance of hard margin SVMs can significantly be improved by our adapted preprocessing step.