Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Universal learning theory

Simple item page
dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-08-20T05:54:32Z
dc.date.available2015-08-20T05:54:32Z
dc.date.issued2011-02
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).en_AU
dc.identifier.isbn978-0-387-30768-8en_AU
dc.identifier.urihttp://hdl.handle.net/1885/14817
dc.publisherSpringer Verlagen_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP0988049en_AU
dc.relation.ispartofEncyclopedia of machine learningen_AU
dc.rights© Springer-Verlag Berlin Heidelberg 2011.en_AU
dc.subjectAlgorithmic probabilityen_AU
dc.subjectRay Solomonoffen_AU
dc.subjectinductionen_AU
dc.subjectpredictionen_AU
dc.subjectdecisionen_AU
dc.subjectactionen_AU
dc.subjectTuring machineen_AU
dc.titleUniversal learning theoryen_AU
dc.typeBook chapteren_AU
local.bibliographicCitation.lastpage1008en_AU
local.bibliographicCitation.startpage1001en_AU
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National Universityen_AU
local.contributor.authoruidu4350841en_AU
local.identifier.doi10.1007/978-0-387-30164-8_861en_AU
local.type.statusAccepted Versionen_AU

Downloads

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
884 B
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ANU Research Publications

DSpace software copyright © 2002-2026 LYRASIS