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Nonparametric graded data processing with back-error propagation networks

Thint Marcus P. , Wang Paul P. , Dollas Apostolos

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URIhttp://purl.tuc.gr/dl/dias/69D8E21E-7A00-4447-BA05-9EC5B1C9983E-
Identifierhttp://www.sciencedirect.com/science/article/pii/0020025593900895-
Identifierhttps://doi.org/10.1016/0020-0255(93)90089-5-
Languageen-
Extent22 pagesen
TitleNonparametric graded data processing with back-error propagation networksen
CreatorThint Marcus P. en
CreatorWang Paul P. en
CreatorDollas Apostolosen
CreatorΔολλας Αποστολοςel
PublisherElsevieren
Content SummaryWe present some computational characteristics of back-error propagation (BEP) networks in processing graded patterns that are otherwise indistinguishable in binary (or bipolar) representations. We address the problem of mapping l-of-m unit gradients with interunit activation profile d to n classes, where maximum noise amplitude of ϵ is permitted within gradient classes. Relations between these parameters and the training period measured in epochs (T) are discussed. Extensions of basic concepts are used to extract embedded feature information in constrained 2D grey-scale patterns and to group and classify distorted pattern clusters whose intraset distances are sometimes greater than interset metrics. Results have been applied to simulation studies in the domain of robotic tactile pattern recognition.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-02-
Date of Publication1993-
SubjectBack-error propagation networksen
Bibliographic CitationM. P. Thint, P. P. Wang and A. Dollas, "Nonparametric graded data processing with back-error propagation networks", Inf. Sci., vol. 67, no. 1-2, pp. 167-188, Jan. 1993. doi:10.1016/0020-0255(93)90089-5en

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