Journals / Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik / 2018 / Cilt: 19 - Sayı: 1

A NOVEL APPROACH FOR LEARNING RATE IN SELF ORGINIZING MAP (SOM)

Pages
144–152
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The effects of this approach have been testedwith the real world problem and different synthetic data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The performance of resultant topological structure of Kohonen Self Organizing Map SOM is highly dependent of the learningrate and neighborhood parameters.
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Key Findings & Data:It has been investigated that in general the learning rate and neighborhood parameters are data independent andpredefined before the training period.
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Conclusion & Impact:The effects of this approach have been testedwith the real world problem and different synthetic data.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The performance of resultant topological structure of Kohonen Self Organizing Map SOM is highly dependent of the learningrate and neighborhood parameters. In literature there are plenty many different types of approaches to and proposals for thoseparameters. It has been investigated that in general the learning rate and neighborhood parameters are data independent andpredefined before the training period. Here in this paper a novel approach has been proposed to change the learning rateparameter according to the interaction of neurons with data. During training, the worst matching neuron also tracked and usedto trace the formation of topological structure of SOM. A slight modification on conventional learning rate with proposedmethod has a considerable influence on resultant topologies in a positive way. The effects of this approach have been testedwith the real world problem and different synthetic data.

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Cite This Article

GERMEN, E. (2022). A NOVEL APPROACH FOR LEARNING RATE IN SELF ORGINIZING MAP (SOM). Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 19 - Sayı: 1, 144–152. https://ajindex.com/articles/438212

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References (9)

  1. UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets/iris. Golli A. Speeding up the self organizing map for dissimilarity data, Proceedings of International Symposium on Applied Stochastic Models and Data Analysis, Brest, France, 2005.
  2. Lawrence RD, Almasi GS, Rushmejer HF. A scalable parallel algorithm for Self organizing maps wwith application to sparce data mining problems, Data Mining and Knowledge Discovery, cilt 3, no. 171, pp. 171-195, 1999.
  3. Mulier F, Ph.D Thesis, University of Minnesota, 1994.
  4. Villmann T, Der R, Hermann M. Martinetz TM. Topology preservation in self-organizing feature maps: exact definition and measurement, IEEE Transactions on Neural Networks, cilt 8, no. 2, pp. 256-266, 1997. Stefanovič P, Kurasova O. Influence of Learning Rates and Neighboring Functions on SelfOrganizing Maps, Laaksonen J, Honkela T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg, 2011.
  5. Germen E, Improving the Resultant Quality of Kohonen's Self Organizing Map Using Stiffness Factor, Wang L, Chen K, Ong YS. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg, 2005.
  6. Germen E, Bilgen S, Statistical Approaches to Determine the Neighborhood Function and Learning Rate in Self Organizing Map, Proceedings of the 1997 International Confererence on Neural Unfırmation Processing and Intelligent Information Systems, Dunedin, New Zealand, 1997.
  7. Mulier F, Cherkassky VS. Statistical analysis of self-organization,» Neural Networks, cilt 8, no. 5, pp. 717-727, 1995.
  8. Kohonen T, The Self Organizing Map, Proceedings of IEEE, pp. 1464-1480, 1990.
  9. Kohonen T, Self Organizing Map, Berlin: Springer Verlag, 1995.