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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.date.accessioned2015-08-19T05:42:55Z
dc.date.available2015-08-19T05:42:55Z
dc.date.issued2011-11
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 problem domains. Additionally, we give a new algorithm for off-line classification, inspired by Solomonoff induction, with good performance on all structured (compressible) problems under reasonable assumptions. This includes a proof of the efficacy of the well-known heuristic of randomly selecting training data in the hope of reducing the misclassification rate.en_AU
dc.identifier.isbn978-3-642-44957-4en_AU
dc.identifier.issn0302-9743en_AU
dc.identifier.urihttp://hdl.handle.net/1885/14800
dc.publisherSpringer Verlagen_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP0988049en_AU
dc.relation.ispartofAlgorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011en_AU
dc.rights© Springer-Verlag Berlin Heidelberg 2013. http://www.sherpa.ac.uk/romeo/issn/0302-9743/..."Author's post-print on any open access repository after 12 months after publication" from SHERPA/RoMEO site (as at 19/08/15)en_AU
dc.subjectSupervised Learningen_AU
dc.subjectKolmogorov complexityen_AU
dc.subjectOccam's Razoren_AU
dc.subjectNo Free Lunchen_AU
dc.titleNo Free Lunch versus Occam’s Razor in supervised learningen_AU
dc.typeConference paperen_AU
local.bibliographicCitation.lastpage235en_AU
local.bibliographicCitation.startpage223en_AU
local.contributor.affiliationLattimore, T., Research School of Computer Science, The Australian National Universityen_AU
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National Universityen_AU
local.contributor.authoruidu4350841en_AU
local.identifier.citationvolume7070en_AU
local.identifier.doi10.1007/978-3-642-44958-1_17en_AU
local.type.statusAccepted Versionen_AU

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