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Zbl 1151.30007
Hofmann, Thomas; Schölkopf, Bernhard; Smola, Alexander J.
Kernel methods in machine learning. (English)
[J] Ann. Stat. 36, No. 3, 1171-1220 (2008). ISSN 0090-5364

Authors' abstract: 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 defined on nonvectorial data.\par We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.
[Miloš Čanak (Beograd)]

MSC 2000:: *30C40 Kernel functions and appl. (one complex variable)
68T05 Learning and adaptive systems

Keywords: machine learning; reproducing kernels; support vector machines; graphical models

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