Statistische Lerntheorie und Empirische Inferenz

Schölkopf, B

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Statistische Lerntheorie und Empirische Inferenz

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Jahrbuch2004-Schoelkopf.pdf
(出版社版), 356KB

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引用

Schölkopf, B. (2004). Statistische Lerntheorie und Empirische Inferenz. Jahrbuch der Max-Planck-Gesellschaft, 2004, 377-382.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-DA7F-7

要旨

Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.