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• W2122528444 abstract "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 <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels." @default.
• W2122528444 created "2016-06-24" @default.
• W2122528444 creator A5047197403 @default.
• W2122528444 creator A5056277018 @default.
• W2122528444 date "2011-10-01" @default.
• W2122528444 modified "2023-10-16" @default.
• W2122528444 title "Compactly Supported Basis Functions as Support Vector Kernels for Classification" @default.
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• W2122528444 doi "https://doi.org/10.1109/tpami.2011.28" @default.
• W2122528444 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21321366" @default.
• W2122528444 hasPublicationYear "2011" @default.
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