Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/3482
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dc.contributor.authorReimers, L-
dc.contributor.authorBOUTEN, Marcus-
dc.contributor.authorVAN ROMPAEY, Bart-
dc.date.accessioned2007-11-28T15:09:47Z-
dc.date.available2007-11-28T15:09:47Z-
dc.date.issued1996-
dc.identifier.citationJOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 29(19). p. 6247-6252-
dc.identifier.issn0305-4470-
dc.identifier.urihttp://hdl.handle.net/1942/3482-
dc.description.abstractPursuing the work of Penney and Sherrington, we determine the optimal continuous-weight perceptron which, on clipping, correctly predicts the largest number of weights for the binary perceptron with maximum stability. We calculate the fraction of correctly predicted binary weights when only the continuous weights stronger than a certain threshold are clipped. We finally carry out simulations for a perceptron with 50 weights to test the practicability of different learning strategies.-
dc.language.isoen-
dc.publisherIOP PUBLISHING LTD-
dc.titleLearning strategy for the binary perceptron-
dc.typeJournal Contribution-
dc.identifier.epage6252-
dc.identifier.issue19-
dc.identifier.spage6247-
dc.identifier.volume29-
local.format.pages6-
dc.description.notesReimers, L, LIMBURGS UNIV CTR,UNIV CAMPUS,B-3590 DIEPENBEEK,BELGIUM.-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1088/0305-4470/29/19/010-
dc.identifier.isiA1996VL96000010-
item.fullcitationReimers, L; BOUTEN, Marcus & VAN ROMPAEY, Bart (1996) Learning strategy for the binary perceptron. In: JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 29(19). p. 6247-6252.-
item.accessRightsClosed Access-
item.contributorReimers, L-
item.contributorBOUTEN, Marcus-
item.contributorVAN ROMPAEY, Bart-
item.fulltextNo Fulltext-
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