Title: Multi-SOMs for classification

Authors: Nils Goerke, Florian Kintzler, Bernd Bruggemann

Addresses: Division of Neural Computation, Department of Computer Science, University of Bonn, Germany. ' Division of Neural Computation, Department of Computer Science, University of Bonn, Germany. ' Division of Neural Computation, Department of Computer Science, University of Bonn, Germany

Abstract: We propose a method to use the classification capabilities of self organising neural networks to extract symbolic information from raw data. The Multi-SOM (M-SOM) approach is a variant of Self Organising Maps (SOM). Multi-SOMS consist of a set of partner SOMs, trained simultaneously and in concurrence to each other, to adapt to different classes. The trained M-SOM transforms the non-linear time series of a strange attractor into a stream of symbols, adequate for further classification or for control tasks. We are convinced, that using the Multi-SOM approch for classification, gives a variety of new applications.

Keywords: self organised learning; self organising maps; SOM; M-SOM; Multi-SOM; classification; non-linear dynamical systems; symbolic representation; automatic learning; real time.

DOI: 10.1504/IJISTA.2007.012485

International Journal of Intelligent Systems Technologies and Applications, 2007 Vol.2 No.2/3, pp.231 - 241

Available online: 19 Feb 2007

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