Abstract
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Keywords
GeneralizationStatistical learning theoryArtificial intelligenceConsistency (knowledge bases)Computer scienceMachine learningStatistical theoryVariety (cybernetics)Process (computing)Algorithmic learning theoryLearning theoryFunction (biology)MathematicsUnsupervised learningStatisticsMathematics education
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Publication Info
- Year
- 1999
- Type
- article
- Volume
- 41
- Issue
- 4
- Pages
- 377-377
- Citations
- 26913
- Access
- Closed
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Cite This
Yuhai Wu,
Vladimir Vapnik
(1999).
Statistical Learning Theory.
Technometrics
, 41
(4)
, 377-377.
https://doi.org/10.2307/1271368
Identifiers
- DOI
- 10.2307/1271368