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Compactly Supported Basis Functions as Support Vector Kernels for Classification
University of Borås, Swedish School of Library and Information Science.
2011 (English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 33, no 10, 2039-2050 p.Article in journal (Refereed) Published
Description
Abstract [en]

Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L(2) space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.

Place, publisher, year, edition, pages
IEEE computer , 2011. Vol. 33, no 10, 2039-2050 p.
Keyword [en]
wavelet kernels, feature engineering, feature correlation, semantic kernels
National Category
Information Studies
Research subject
Library and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-1234DOI: 10.1109/TPAMI.2011.28Local ID: 2320/10236OAI: oai:DiVA.org:hb-1234DiVA: diva2:869258
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2017-01-03Bibliographically approved

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  • apa
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