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Recent Results in Universal and Non-Universal Induction

Authors: Jan Poland

Published in: Dagstuhl Seminar Proceedings, Volume 6051, Kolmogorov Complexity and Applications (2006)

Abstract

We present and relate recent results in prediction based on countable classes of either probability (semi-)distributions or base predictors. Learning by Bayes, MDL, and stochastic model selection will be considered as instances of the first category. In particular, we will show how analog assertions to Solomonoff's universal induction result can be obtained for MDL and stochastic model selection. The second category is based on prediction with expert advice. We will present a recent construction to define a universal learner in this framework.

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Jan Poland. Recent Results in Universal and Non-Universal Induction. In Kolmogorov Complexity and Applications. Dagstuhl Seminar Proceedings, Volume 6051, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{poland:DagSemProc.06051.12,
  author =	{Poland, Jan},
  title =	{{Recent Results in Universal and Non-Universal Induction}},
  booktitle =	{Kolmogorov Complexity and Applications},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6051},
  editor =	{Marcus Hutter and Wolfgang Merkle and Paul M.B. Vitanyi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06051.12},
  URN =		{urn:nbn:de:0030-drops-6355},
  doi =		{10.4230/DagSemProc.06051.12},
  annote =	{Keywords: Bayesian learning, MDL, stochastic model selection, prediction with expert advice, universal learning, Solomonoff induction}
}

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Recent Results in Universal and Non-Universal Induction

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