License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.06051.12
URN: urn:nbn:de:0030-drops-6355
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2006/635/
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Poland, Jan

Recent Results in Universal and Non-Universal Induction

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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.

BibTeX - Entry

@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/opus/volltexte/2006/635},
  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}
}

Keywords: Bayesian learning, MDL, stochastic model selection, prediction with expert advice, universal learning, Solomonoff induction
Collection: 06051 - Kolmogorov Complexity and Applications
Issue Date: 2006
Date of publication: 31.07.2006

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