The Annals of Statistics
- Ann. Statist.
- Volume 36, Number 3 (2008), 1171-1220.
Kernel methods in machine learning
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.
Ann. Statist. Volume 36, Number 3 (2008), 1171-1220.
First available in Project Euclid: 26 May 2008
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 30C40: Kernel functions and applications
Secondary: 68T05: Learning and adaptive systems [See also 68Q32, 91E40]
Hofmann, Thomas; Schölkopf, Bernhard; Smola, Alexander J. Kernel methods in machine learning. Ann. Statist. 36 (2008), no. 3, 1171--1220. doi:10.1214/009053607000000677. https://projecteuclid.org/euclid.aos/1211819561.