Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/54452
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dc.contributor.authorRaudys, Šarūnas-
dc.contributor.authorTamošiūnaitė, Minija-
dc.coverage.spatialDE-
dc.date.accessioned2018-10-07T00:12:54Z-
dc.date.available2018-10-07T00:12:54Z-
dc.date.issued2000-
dc.identifier.isbn9783540679462-
dc.identifier.issn03029743-
dc.identifier.otherVDU02-000008330-
dc.identifier.urihttps://doi.org/10.1007/3-540-44522-6_75-
dc.description.abstractThe hypothesis is that in the lowest bidden layers of biological systems "local subnetworks" are smoothing an input signal. The smoothing accuracy may serve as a feature to feed the subsequent layers of the pattern classification network. The present paper suggests a multistage supervised and "unsupervised" training approach for design and training of multilayer feed-forward networks. Following to the methodology used in the statistical pattern recognition systems we split functionally the decision making process into two stages. In an initial stage, we smooth the input signal in a number of different ways and, in the second stage, we use the smoothing accuracy as anew feature to perform a final classificationen
dc.description.sponsorshipTaikomosios informatikos katedra-
dc.description.sponsorshipVytauto Didžiojo universitetas-
dc.format.extentp. 727-736-
dc.language.isoen-
dc.relation.ispartofAdvances in pattern recognition : joint IAPR international workshops SSPR 2000 and SPR 2000 Alicante, Spain, August 30 – September 1, 2000 : proceedings. Berlin, Heidelberg : Springer, 2000-
dc.relation.ispartofseries(Lecture notes in computer science. Vol. 1876 0302-9743)-
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)-
dc.relation.isreferencedbySpringerLINK-
dc.relation.isreferencedbyScopus-
dc.subjectSignal classificationen
dc.subjectNeural networken
dc.subjectPattern recognitionen
dc.subject.classificationStraipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)-
dc.subject.otherInformatika / Informatics (N009)-
dc.titleBiologically inspired architecture of feedforward networks for signal classificationen
dc.typeresearch article-
dc.identifier.doihttps://doi.org/10.1007/3-540-44522-6_75-
dc.identifier.isiWOS:000171155700075-
dcterms.bibliographicCitation16-
dc.date.updated2021-04-21T13:28Z-
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local.typeS-
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptTaikomosios informatikos katedra-
crisitem.author.deptTaikomosios informatikos katedra-
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications
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