Work

Recent Results in Universal and Non-Universal Induction

Year: 2006

Type: article

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.

Source: DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

DOI: 10.4230/dagsemproc.06051.12

Author: Poland, Jan

Institution: Hokkaido University

Language: English

Topic: Computability, Logic, AI Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

OpenAlex ID: W85077790