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  Learning with Transformation Invariant Kernels

Walder, C., & Chapelle, O.(2007). Learning with Transformation Invariant Kernels (165).

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 Urheber:
Walder, C1, 2, Autor           
Chapelle, O1, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

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 Zusammenfassung: Abstract. This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation invariant, only conditionally positive definite (c.p.d.) ones. Accordingly, we discuss the c.p.d. case and provide some novel analysis, including an elementary derivation of a c.p.d. representer theorem. On the practical side, we give a support vector machine (s.v.m.) algorithm for arbitrary c.p.d. kernels. For the thin-plate kernel this leads to a classifier with only one parameter (the amount of regularisation), which we demonstrate to be as effective as an s.v.m. with the Gaussian kernel, even though the Gaussian involves a second parameter (the length scale).

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 Datum: 2007-09
 Publikationsstatus: Erschienen
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 Identifikatoren: Reportnr.: 165
BibTex Citekey: 4737
 Art des Abschluß: -

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