Improved SOM learning using simulated annealingFiannaa, A., Di Fatta, G., Gaglio, S., Rizzo, R. and Urso, A. (2007) Improved SOM learning using simulated annealing. In: Sá, J. M. d., Alexandre, L. A., Duch, W. and Mandic, D. (eds.) Artificial neural networks – ICANN 2007 : 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I. Lecture notes in computer science (4668). Springer-Verlag, Berlin, pp. 279-288. ISBN 9783540746898 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1007/978-3-540-74690-4 Abstract/SummarySelf-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better performance in terms of matching of input data and regularity of the obtained map. An advantage of the proposed technique is that it preserves the simplicity of the basic algorithm. Several tests, carried out on different large datasets, demonstrate the effectiveness of the proposed algorithm in comparison with the original SOM and with some of its modification introduced to speed-up the learning.
Altmetric Related URLs Deposit Details References University Staff: Request a correction | Centaur Editors: Update this record |
Lists
Lists
