Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/182
Title: Improving the Performance of Resource Allocation Networks through Hierarchical Clustering of High-Dimensional Data
Authors: Tsapatsoulis, Nicolas 
Wallace, Manolis 
Kasderidis, Stathis 
Keywords: Neural networks (Computer science)--Congresses
Issue Date: 2003
Publisher: Springer Berlin / Heidelberg
Source: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, p.173
Series/Report no.: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003;
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.)
Description: ICANN/ICONIP 2003,(2003,Istanbul,Turkey)
URI: http://ktisis.cut.ac.cy/handle/10488/182
ISBN: 9783540404088
DOI: 10.1007/3-540-44989-2_22
Rights: © Springer
Type: Book Chapter
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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