DagSemProc.06051.12.pdf
- Filesize: 219 kB
- 11 pages
Document
Recent Results in Universal and Non-Universal Induction
Author
-
Part of:
Volume:
Dagstuhl Seminar Proceedings, Volume 6051
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2006-07-31
Cite As Get BibTex
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
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.
Subject Classification
Keywords
- Bayesian learning
- MDL
- stochastic model selection
- prediction with expert advice
- universal learning
- Solomonoff induction
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads