DagSemProc.06051.12.pdf
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Recent Results in Universal and Non-Universal Induction
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Dagstuhl Seminar Proceedings, Volume 6051
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2006-07-31
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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)
https://doi.org/10.4230/DagSemProc.06051.12
https://doi.org/10.4230/DagSemProc.06051.12
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.
Keywords
- Bayesian learning
- MDL
- stochastic model selection
- prediction with expert advice
- universal learning
- Solomonoff induction
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