Kernel methods in machine learning

被引：1318

作者：

Hofmann, Thomas ^{[1
]}

Schoelkopf, Bernhard ^{[2
]}

Smola, Alexander J. ^{[3
]}

机构：

[1] Tech Univ Darmstadt, Dept Comp Sci, Darmstadt, Germany

[2] Max Planck Inst Biol Cybernet, Tubingen, Germany

[3] Natl ICT Australia, Stat Machine Learning Program, Canberra, ACT, Australia

来源：

ANNALS OF STATISTICS | 2008年 / 36卷 / 03期

关键词：

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

D O I：

10.1214/009053607000000677

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：1171 / 1220

页数：50

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