Recent Results in Universal and Non-Universal Induction

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.

• Type: Conference
• Year: 2006
• Where: DAGSTUHL
• Authors: Jan Poland
• Keywords: Base Predictors, Countable Classes, DAGSTUHL 2006, DAGSTUHL 2007, Stochastic Model Selection