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dc.contributor.authorKeeni, Kanad
dc.contributor.authorNakayama, Kenji
dc.contributor.authorShimodaira, Hiroshi
dc.coverage.spatial4en
dc.date.accessioned2006-05-12T12:51:06Z
dc.date.available2006-05-12T12:51:06Z
dc.date.issued1998-08
dc.identifier.citationPattern Recognition, 1998. Proceedings. Fourteenth International Conference on, Volume 2, 16-20 Aug. 1998 Page(s):1568 - 1571en
dc.identifier.otherDigital Object Identifier 10.1109/ICPR.1998.712010
dc.identifier.urihttp://ieeexplore.ieee.org/servlet/opac?punumber=5726
dc.identifier.urihttp://hdl.handle.net/1842/1037
dc.description.abstractThis study high lights on the subject of weight initialization in back-propagation feed-forward networks. Training data is analyzed and the notion of critical points is introduced for determining the initial weights and the number of hidden units. The proposed method has been applied to artificial data and the publicly available cancer database. The experimental outcomes indicate that the proposed method reduces training time and results in better solution.en
dc.format.extent388475 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen
dc.titleAutomatic Generation of Initial Weights and Estimation of Hidden Units for Pattern Classifcation Using Neural Networksen
dc.typeConference Paperen


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