Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification


Neumann, Julia ; Schnörr, Christoph ; Steidl, Gabriele


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URL: https://ub-madoc.bib.uni-mannheim.de/734
URN: urn:nbn:de:bsz:180-madoc-7347
Document Type: Working paper
Year of publication: 2003
The title of a journal, publication series: Technical Reports
Volume: 03-005
Place of publication: Mannheim
Publication language: English
Institution: School of Business Informatics and Mathematics > Sonstige - Fakultät für Mathematik und Informatik
MADOC publication series: Veröffentlichungen der Fakultät für Mathematik und Informatik > Institut für Informatik > Technical Reports
Subject: 004 Computer science, internet
Subject headings (SWD): Klassifikation , Wavelet
Keywords (English): filter design , signal and image classification , wavelets , Support Vector Machine
Abstract: For hybrid wavelet - large margin classifiers, adapting the wavelet may significantly improve the classification performance. We propose to select the wavelet with respect to a large margin classifier and data to improve class separability and minimise the generalisation error. In this paper, we show that this wavelet adaptation problem can be formulated as an optimisation problem with polynomial objective function and investigate some techniques to solve it. In particular, we propose an adaptive grid search algorithm that efficiently solves the problem compared with standard optimisation techniques.
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Neumann, Julia ; Schnörr, Christoph ; Steidl, Gabriele (2003) Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification. Open Access Technical Reports Mannheim 03-005 [Working paper]
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