Kernel methods in machine learning

• 成果类型: Review
• 署名作者: Hofmann, Thomas; Schoelkopf, Bernhard; Smola, Alexander J.
• 署名单位: Technical University of Darmstadt; Max Planck Society; NICTA
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053607000000677
• 发表日期: 2008
• 页码: 1171-1220
• 关键词: Support vector machines; component analysis; canonical-analysis; regression; approximation; regularization; Connection; SEPARATION; families; features

摘要

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.