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Kernel methods in machine learning

Hofmann, Thomas; Schölkopf, Bernhard; Smola, Alexander J.


We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Journal article
Source: Annals of Statistics
DOI: 10.1214/009053607000000677


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