Improving the performance of resource allocation networks through hierarchical clustering of high-dimensional data

Tsapatsoulis, N; Wallace, M; Kasderidis, S

dc.contributor.author	Tsapatsoulis, N	en
dc.contributor.author	Wallace, M	en
dc.contributor.author	Kasderidis, S	en
dc.date.accessioned	2014-03-01T01:19:02Z
dc.date.available	2014-03-01T01:19:02Z
dc.date.issued	2003	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15338
dc.subject	Ambient Intelligence	en
dc.subject	Hierarchical Clustering	en
dc.subject	High Dimensional Data	en
dc.subject	High Dimensionality	en
dc.subject	Intelligent System	en
dc.subject	Ionosphere	en
dc.subject	Knowledge Based System	en
dc.subject	Knowledge Modelling	en
dc.subject	Network Architecture	en
dc.subject	Resource Allocation	en
dc.subject	Unsupervised Clustering	en
dc.subject	Neural Network	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	NEURAL NETWORKS	en
dc.subject.other	SYSTEMS	en
dc.title	Improving the performance of resource allocation networks through hierarchical clustering of high-dimensional data	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/3-540-44989-2_103	en
heal.identifier.secondary	http://dx.doi.org/10.1007/3-540-44989-2_103	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Adaptivity to non-stationary contexts is a very important property for intelligent systems in general, as well as to a variety of applications of knowledge based systems in era of ""ambient intelligence"". In this paper we present a modified Resource Allocating Network architecture that allows for online adaptation and knowledge modelling through its adaptive structure. As in any neural network system proper parameter initialization reduces training time and effort. However, in RAN architectures, proper parameter initialization also leads to compact modelling (less hidden nodes) of the process under examination, and consequently to better generalization. In the cases of high-dimensional data parameter initialization is both difficult and time consuming. In the proposed scheme a high - dimensional, unsupervised clustering method is used to properly initialize the RAN architecture. Clusters correspond to the initial nodes of RAN, while output layer weights are also extracted from the clustering procedure. The efficiency of the proposed method has been tested on several classes of publicly available data (iris, ionosphere, etc.) © Springer-Verlag Berlin Heidelberg 2003.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/3-540-44989-2_103	en
dc.identifier.isi	ISI:000185378100103	en
dc.identifier.volume	2714	en
dc.identifier.spage	867	en
dc.identifier.epage	874	en