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Kernel methods in machine learning. (English) Zbl 1151.30007
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.
We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.

MSC:
30C40 Kernel functions in one complex variable and applications
68T05 Learning and adaptive systems in artificial intelligence
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