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URL:
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https://ub-madoc.bib.uni-mannheim.de/1611
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URN:
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urn:nbn:de:bsz:180-madoc-16118
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Document Type:
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Working paper
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Year of publication:
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2001
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Sonstige - Fakultät für Mathematik und Informatik
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MADOC publication series:
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Veröffentlichungen der Fakultät für Mathematik und Informatik > Institut für Mathematik > Mannheimer Manuskripte
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Subject:
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510 Mathematics
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Classification:
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MSC:
65T60 90C90 90C20 90C30 90C59 ,
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Subject headings (SWD):
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Support-Vektor-Maschine , Hilbert-Kern , Wavelet , Frame <Mathematik> , Radialfunktion
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Keywords (English):
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Support vector machines , radial basis functions , reproducing kernel Hilbert spaces , wavelets , adapted filter banks , frames , waveform recognition
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Abstract:
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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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Additional information:
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[Working paper]
![[img]](https://madoc.bib.uni-mannheim.de/style/images/fileicons/application_pdf.png)
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