Statistical learning theory
From Wikipedia, the free encyclopedia
Statistical learning theory is an ambiguous term.
- It may refer to computational learning theory, which is a sub-field of theoretical computer science that studies how algorithms can learn from data.
- It may refer to Vapnik–Chervonenkis theory, which is a specific approach to computational learning theory, proposed by Vladimir Vapnik and Alexey Chervonenkis.
- It may refer to the updating of probability distributions (that represent beliefs) as new information is gained, using Bayes' theorem, as in recursive Bayesian estimation.
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