Improved SOM learning using simulated annealing

Download

Full text not archived in this repository.

Advice

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Tools

Lists

Fiannaa, 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 doi: 10.1007/978-3-540-74690-4

Abstract/Summary

Self-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 Badge

Item Type	Book or Report Section
URI	https://reading-clone.eprints-hosting.org/id/eprint/6129
Identification Number/DOI	10.1007/978-3-540-74690-4
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Springer-Verlag
Download/View statistics	View download statistics for this item

Related URLs

Deposit Details

Date Deposited:	06 Sep 2010 09:02	Date item deposited into CentAUR
Last Modified:	09 Jul 2021 07:18	Date item last modified

References

1. Rizzo, R., Chella, A.: A Comparison between Habituation and Conscience Mechanism in Self-Organizing Maps. IEEE Transactions on neural networks 17(3), 807–810 (2006) 2. DeSieno, D.: Adding a conscience to copetitive learning. In: Proc. ICNN’88, International conference on Neuroal Networks, pp. 117–124. IEEE Computer Society Press, Piscataway, NJ (1988) 3. Berglund, E., Sitte, J.: The parameterless self-organizing map algorithm. IEEE Transactions on neural networks 17(2), 305–316 (2006) 4. Haese, K.: Auto-SOM: recursive parameter estimation for guidance of selforganizing feature maps. Neural Comput. 13(3), 595–619 (2001) 5. Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983) 6. Metropolis, N., Rosenbluth, A.W., Rosenbluth,M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087– 1092 (1953) 7. Graepel, T., Burger, M., Obermayer, K.: Self-organizing maps: generalizations and new optimization techniques. Neurocomputing 21, 173–190 (1998) 8. Douzono, H., Hara, S., Noguchi, Y.: A Clustering Method of Chromosome Fluorescence Profiles Using Modified Self Organizing Map Controlled by Simulated Annealing. In: IJCNN’00. IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 4 (2000) 9. Haese, K.: Kalman filter implementation of self-organizing feature maps. Neural Comput. 11(5), 1211–1233 (1999) 10. Haese, K.: Self-organizing feature map with self-adjusting learning parameters. IEEE Transactions on Neural Network 9(6), 1270–1278 (1998) 11. Berglund, E., Sitte, J.: The parameter-less SOM algorithm. In: Proc. ANZIIS, pp. 159–164 (2003) 12. Kohonen, T.: Self-Organizing Maps. Springer Verlag, Berlin (1995) 13. Van Hulle, M. (ed.): Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization. John Wiley, New York (2000) 14. Ingber, L.: Very fast simulated re-annealing. Journal of Mathl. Comput. Modelling 12(8), 967–973 (1989) 15. Ingber, L.: Adaptive simulated annealing (ASA): lessons learned. J. Control and Cybernetics 25(1), 33–54 (1996) 16. Goppert, J., Rosenstiel, W.: Regularized SOM-Training: A Solution to the Topology-Approximation dilemma, University of Tbingen (1996) 17. National Cancer Institute, DTP AIDS antiviral screen dataset [online], http://dtp.nci.nih.gov/docs/aids/aids/data.html 18. Di Fatta, G., Fiannaca, A., Rizzo, R., Urso, A., Berthold, M.R., Gaglio, S.: Context-Aware Visual Exploration of Molecular Databases. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 136–141. Springer, Heidelberg (2006)

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar