Conference paper

Publication year: 2005

Pages: 39-71

Journal: Neurocomputing

Volume number: 64

ISSN: 0925-2312

eISSN: 1872-8286

DOI Link: https://doi.org/10.1016/j.neucom.2004.11.014

Conference: 12th European Symposium on Artificial Neural Networks (ESANN)

Publisher: Elsevier

Abstract: 
The recent merging self-organizing map (MSOM) for unsupervised sequence processing constitutes a fast, intuitive, and powerful unsupervised learning model. In this paper, we investigate its theoretical and practical properties. Particular focus is put on the context established by the self-organizing MSOM, and theoretic results on the representation capabilities and the MSOM training dynamic are presented. For practical studies, the context model is combined with the neural gas vector quantizer to obtain merging neural gas (MNG) for temporal data. The suitability of MNG is demonstrated by experiments with artificial and real-world sequences with one- and multi-dimensional inputs from discrete and continuous domains. (c) 2004 Elsevier B.V. All rights reserved.

Harvard Citation style: Strickert, M. and Hammer, B. (2005) Merge SOM for temporal data, Neurocomputing, 64, pp. 39-71. https://doi.org/10.1016/j.neucom.2004.11.014

APA Citation style: Strickert, M., & Hammer, B. (2005). Merge SOM for temporal data. Neurocomputing. 64, 39-71. https://doi.org/10.1016/j.neucom.2004.11.014