Connectionist models for formal knowledge adaptation

Kollia, I; Simou, N; Stamou, G; Stafylopatis, A

dc.contributor.author	Kollia, I	en
dc.contributor.author	Simou, N	en
dc.contributor.author	Stamou, G	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:46:05Z
dc.date.available	2014-03-01T02:46:05Z
dc.date.issued	2009	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32525
dc.subject	Artificial Intelligent	en
dc.subject	Cluster System	en
dc.subject	connectionist models	en
dc.subject	Image Segmentation	en
dc.subject	Knowledge Representation	en
dc.subject	Multimedia Application	en
dc.subject	Neural Network	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Classification and clustering	en
dc.subject.other	Connectionist models	en
dc.subject.other	Formal knowledge	en
dc.subject.other	Image segments	en
dc.subject.other	Multimedia applications	en
dc.subject.other	Novel architecture	en
dc.subject.other	Proposed architectures	en
dc.subject.other	Recent trends	en
dc.subject.other	Symbolic knowledge	en
dc.subject.other	Backpropagation	en
dc.subject.other	Knowledge representation	en
dc.subject.other	Multimedia systems	en
dc.subject.other	Neural networks	en
dc.title	Connectionist models for formal knowledge adaptation	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-04277-5_47	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-04277-5_47	en
heal.publicationDate	2009	en
heal.abstract	Both symbolic knowledge representation systems and artificial neural networks play a significant role in Artificial Intelligence. A recent trend in the field aims at interweaving these techniques, in order to improve robustness and performance of classification and clustering systems. In this paper, we present a novel architecture based on the connectionist adaptation of ontological knowledge. The proposed architecture was used effectively to improve image segment classification within a multimedia application scenario. © 2009 Springer Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-04277-5_47	en
dc.identifier.volume	5769 LNCS	en
dc.identifier.issue	PART 2	en
dc.identifier.spage	465	en
dc.identifier.epage	474	en