Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/41118
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dc.titleGenerality's price inescapable deficiencies in machine-learned programs
dc.contributor.authorCase, J.
dc.contributor.authorChen, K.-J.
dc.contributor.authorJain, S.
dc.contributor.authorMerkle, W.
dc.contributor.authorRoyer, J.S.
dc.date.accessioned2013-07-04T08:20:02Z
dc.date.available2013-07-04T08:20:02Z
dc.date.issued2003
dc.identifier.citationCase, J.,Chen, K.-J.,Jain, S.,Merkle, W.,Royer, J.S. (2003). Generality's price inescapable deficiencies in machine-learned programs. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) 2777 : 684-698. ScholarBank@NUS Repository.
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41118
dc.description.abstractThis paper investigates some delicate tradeoffs between the generality of an algorithmic learning device and the quality of the programs it learns successfully. There are results to the effect that, thanks to small increases in generality of a learning device, the computational complexity of some successfully learned programs is provably unalterably suboptimal. There are also results in which the complexity of successfully learned programs is asymptotically optimal and the learning device is general, but, still thanks to the generality, some of those optimal, learned programs are provably unalterably information deficient - in some cases, deficient as to safe, algorithmic extractability/provability of the fact that they are even approximately optimal. The paper is on the borderline between learning theory, complexity theory and logic.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
dc.description.volume2777
dc.description.page684-698
dc.description.codenLNAIE
dc.identifier.isiutNOT_IN_WOS
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