期刊
ADVANCES IN COMPUTATIONAL MATHEMATICS
卷 13, 期 1, 页码 1-50出版社
SPRINGER
DOI: 10.1023/A:1018946025316
关键词
regularization; Radial Basis Functions; Support Vector Machines; Reproducing Kernel Hilbert Space; Structural Risk Minimization
Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples - in particular, the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines. We review both formulations in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics. The emphasis is on regression: classification is treated as a special case.
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