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Universal learning theory

Hutter, Marcus

Description

Universal (machine) learning is concerned with the development and study of algorithms that are able to learn from data in a very large range of environments with as few assumptions as possible. The class of environments typically considered includes all computable stochastic processes. The investigated learning tasks range from inductive inference, sequence prediction, sequential decisions, to (re)active problems like reinforcement learning (Hutter, 2005), but also include clustering,...[Show more]

dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-08-20T05:54:32Z
dc.date.available2015-08-20T05:54:32Z
dc.identifier.isbn978-0-387-30768-8
dc.identifier.urihttp://hdl.handle.net/1885/14817
dc.description.abstractUniversal (machine) learning is concerned with the development and study of algorithms that are able to learn from data in a very large range of environments with as few assumptions as possible. The class of environments typically considered includes all computable stochastic processes. The investigated learning tasks range from inductive inference, sequence prediction, sequential decisions, to (re)active problems like reinforcement learning (Hutter, 2005), but also include clustering, regression, and others (Li & Vitányi, 2008).
dc.publisherSpringer Verlag
dc.relation.ispartofEncyclopedia of machine learning
dc.rights© Springer-Verlag Berlin Heidelberg 2011.
dc.subjectAlgorithmic probability
dc.subjectRay Solomonoff
dc.subjectinduction
dc.subjectprediction
dc.subjectdecision
dc.subjectaction
dc.subjectTuring machine
dc.titleUniversal learning theory
dc.typeBook chapter
dc.date.issued2011-02
local.type.statusAccepted Version
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
dc.relationhttp://purl.org/au-research/grants/arc/DP0988049
local.bibliographicCitation.startpage1001
local.bibliographicCitation.lastpage1008
local.identifier.doi10.1007/978-0-387-30164-8_861
CollectionsANU Research Publications

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