Statistical Learning Theory: Models, Concepts, and Results

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dc.creatorvon Luxburg, Ulrike
dc.creatorSchoelkopf, Bernhard
dc.date2008-10-27
dc.date.accessioned2026-07-07T10:13:21Z
dc.date.available2026-07-07T10:13:21Z
dc.descriptionStatistical learning theory provides the theoretical basis for many of today's machine learning algorithms. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We target at a broad audience, not necessarily machine learning researchers. This paper can serve as a starting point for people who want to get an overview on the field before diving into technical details.
dc.identifierhttps://arxiv.org/abs/0810.4752
dc.identifierhttp://arxiv.org/abs/0810.4752
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/172501
dc.subjectMachine Learning
dc.subjectStatistics Theory
dc.titleStatistical Learning Theory: Models, Concepts, and Results
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