Multi-network architecture for high generalization in pattern recognition with back-propagation neural network modules

Tzafestas, SG; Anthopoulos, Y

dc.contributor.author	Tzafestas, SG	en
dc.contributor.author	Anthopoulos, Y	en
dc.date.accessioned	2014-03-01T02:41:09Z
dc.date.available	2014-03-01T02:41:09Z
dc.date.issued	1996	en
dc.identifier.issn	08843627	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30394
dc.subject	backpropagation	en
dc.subject	backpropagation neural network	en
dc.subject	Function Approximation	en
dc.subject	Network Architecture	en
dc.subject	Networked Learning	en
dc.subject	Pattern Classification	en
dc.subject	Pattern Recognition	en
dc.subject.other	Algorithms	en
dc.subject.other	Approximation theory	en
dc.subject.other	Backpropagation	en
dc.subject.other	Computer architecture	en
dc.subject.other	Image analysis	en
dc.subject.other	Learning systems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Random processes	en
dc.subject.other	Multinetwork architecture	en
dc.subject.other	Pattern recognition	en
dc.title	Multi-network architecture for high generalization in pattern recognition with back-propagation neural network modules	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICSMC.1996.569887	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICSMC.1996.569887	en
heal.publicationDate	1996	en
heal.abstract	Back-propagation networks are the most popular multi-layer networks, used for either function approximation or pattern classification. They are trained and tested using two disjoint sets of patterns drawn randomly from the pattern space. In many cases, the overtraining phenomenon occurs i.e. the network learns to produce the proper output for the patterns to which it has been trained but it produces meaningless outputs for unforeseen patterns. In this paper, the overtraining phenomenon is analyzed in depth, and an alternative architecture with increased generalization ability is proposed.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics	en
dc.identifier.doi	10.1109/ICSMC.1996.569887	en
dc.identifier.volume	1	en
dc.identifier.spage	741	en
dc.identifier.epage	746	en