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Author Salas, R.; Allende, H.; Moreno, S.; Saavedra, C.
Title Flexible Architecture of Self Organizing Maps for changing environments Type
Year 2005 Publication Lecture Notes in Computer Sciences Abbreviated Journal Lect. Notes Comput. Sc.
Volume 3773 Issue Pages 642-653
Keywords catastrophic interference; Artificial Neural Networks; Self Organizing Maps; pattern recognition
Abstract Catastrophic Interference is a well known problem of Artificial Neural Networks (ANN) learning algorithms where the ANN forget useful knowledge while learning from new data. Furthermore the structure of most neural models must be chosen in advance. In this paper we introduce a hybrid algorithm called Flexible Architecture of Self Organizing Maps (FASOM) that overcomes the Catastrophic Interference and preserves the topology of Clustered data in changing environments. The model consists in K receptive fields of self organizing maps. Each Receptive Field projects high-dimensional data of the input space onto a neuron position in a low-dimensional output space grid by dynamically adapting its structure to a specific region of the input space. Furthermore the FASOM model automatically finds the number of maps and prototypes needed to successfully adapt to the data. The model has the capability of both growing its structure when novel clusters appears and gradually forgets when the data volume is reduced in its receptive fields. Finally we show the capabilities of our model with experimental results using synthetic sequential data sets and real world data.
Address Univ Valparaiso, Dept Comp, Valparaiso, Chile, Email: rodrigo.salas@uv.cl
Corporate Author Thesis
Publisher Springer-Verlag Berlin Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN Medium
Area Expedition Conference Progress In Pattern Recognition
Notes WOS:000234341500067 Approved
Call Number UAI @ eduardo.moreno @ Serial 44
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