1998 | OriginalPaper | Buchkapitel
Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces
verfasst von : Bernhard Schölkopf, Phil Knirsch, Alex Smola, Chris Burges
Erschienen in: Mustererkennung 1998
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Kernel-based learning methods provide their solutions as expansions in terms of a kernel. We consider the problem of reducing the computational complexity of evaluating these expansions by approximating them using fewer terms. As a by-product, we point out a connection between clustering and approximation in reproducing kernel Hilbert spaces generated by a particular class of kernels.