Hybrid Wavelet-Support Vector Classifiers

Strauß, Daniel J. ; Steidl, Gabriele

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URL: https://ub-madoc.bib.uni-mannheim.de/1611
URN: urn:nbn:de:bsz:180-madoc-16118
Document Type: Working paper
Year of publication: 2001
The title of a journal, publication series: None
Publication language: English
Institution: School of Business Informatics and Mathematics > Sonstige - Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik
MADOC publication series: Veröffentlichungen der Fakultät für Mathematik und Informatik > Institut für Mathematik > Mannheimer Manuskripte
Subject: 510 Mathematics
Classification: MSC: 65T60 90C90 90C20 90C30 90C59 ,
Subject headings (SWD): Support-Vektor-Maschine , Hilbert-Kern , Wavelet , Frame <Mathematik> , Radialfunktion
Keywords (English): Support vector machines , radial basis functions , reproducing kernel Hilbert spaces , wavelets , adapted filter banks , frames , waveform recognition
Abstract: 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 SVMs for waveform classification. The adaptation of the wavelet decompositions is tailormade for SVMs 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 SVMs can significantly be improved by our adapted preprocessing step.
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