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No free lunch versus Occam's Razor in supervised learning

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dc.contributor.authorLattimore, Tor
dc.contributor.authorHutter, Marcus
dc.coverage.spatialMelbourne Australia
dc.date.accessioned2015-12-13T22:55:46Z
dc.date.createdNovember 30-December 2 2011
dc.date.issued2013
dc.date.updated2015-12-11T11:12:08Z
dc.description.abstractThe No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the case for a universal bias allowing an algorithm to succeed in all interesting
dc.identifier.isbn9783642449574
dc.identifier.urihttp://hdl.handle.net/1885/82661
dc.publisherConference Organising Committee
dc.relation.ispartofseriesRay Solomonoff 85th Memorial Conference on Algorithmic Probability and Friends: Bayesian Prediction and Artificial Intelligence
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleNo free lunch versus Occam's Razor in supervised learning
dc.typeConference paper
local.bibliographicCitation.lastpage235
local.bibliographicCitation.startpage223
local.contributor.affiliationLattimore, Tor, College of Engineering and Computer Science, ANU
local.contributor.affiliationHutter, Marcus, College of Engineering and Computer Science, ANU
local.contributor.authoruidLattimore, Tor, u4194344
local.contributor.authoruidHutter, Marcus, u4350841
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080401 - Coding and Information Theory
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationf5625xPUB10890
local.identifier.doi10.1007/978-3-642-44958-1-17
local.identifier.scopusID2-s2.0-84893164552
local.type.statusPublished Version

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