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Hybrid Wavelet-support Vector Classification of Waveforms by Daniel J. Strauss; Gabriele Steidl is a Mathematics article available to read on EtoBox.

What is Hybrid Wavelet-support Vector Classification of Waveforms about?

The support vector machine (SVM) represents a new and very promising technique for machine learning tasks involving classiÿcation, 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 classiÿcation. The adaptation of the wavelet decompositions is tailored for hard margin SV classiÿers with radial basis functions as kernels. It allows the optimization of the representation of the data before training the SVM and does not su er from computationally expensive validation techniques. We assess the performance of our algorithm against the background of current concerns in medical diagnostics, namely the classiÿcation of endocardial electrograms and the detection of otoacoustic emissions. Here the performance of hard margin SVMs can signiÿcantly be improved by our adapted preprocessing step.

Who reads Hybrid Wavelet-support Vector Classification of Waveforms?

Author

Daniel J. Strauss; Gabriele Steidl

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0377-0427)

Published

2002

Language

EN

Field

Mathematics (Physical Sciences)