Abstract
A novel self-organising map (SOM) algorithm based on the snap-drift neural network (SDSOM) is proposed. The modal learning algorithm deploys a combination of the snap-drift modes; fuzzy AND (or Min) learning (snap), and Learning Vector Quantisation (drift). The performance of the algorithm is tested on several well known data sets and compared with the traditional Kohonen SOM algorithm. It is found that the snap mode makes the learning in SDSOM faster than the Kohonen SOM, and that it leads to the formation of more compact maps. When using the maps for classification, SDSOM gives better performance, based on labelled winning nodes, than Kohonen SOM on a variety of data sets.
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Lee, S.W., Palmer-Brown, D., Tepper, J., Roadknight, C.M.: Snap Drift: Realtime Performance guided Learning. In: International Joint Conference on Neural Networks IJCNN, Portland, Oregon, vol. 2, pp. 1412–1416 (2003)
Lee, S.W., Palmer-Brown, D., Roadknight, C.M.: Performance guided Neural Network for Rapidly Self Organising Active Network Management. Neurocomputing 61C, 5–20 (2004)
Lee, S.W., Palmer-Brown, D.: Phonetic Feature Discovery in Speech using Snap-Drift. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006, Part II. LNCS, vol. 4132, pp. 952–962. Springer, Heidelberg (2006)
Ekpenyong, F., Palmer-Brown, D., Brimicombe, A.: Extracting road information from recorded GPS data using snap-drift neural network. Neurocomputing 73, 24–36 (2009)
Palmer-Brown, D., Draganova, C., Lee, S.W.: Snap-Drift Neural Network for Selecting Student Feedback. In: International Joint Conference on Neural Networks IJCNN, Atlanta, pp. 391–398 (2009)
Kohonen, T.: Self-Organisation and Asssociative Memory, 3rd edn. Springer, Heilderberg (1989)
Ritter, H., Kohonen, T.: Self-Organizing Semantic Maps. Biological Cybernetics 61, 241–254 (1989)
Kohonen, T.: Learning Vector Quantisation. Helsinki University of Technology, Laboratory of Computer and Information Science, Report TKK-F-A-601 (1986)
Kohonen, T.: Learning Vector Quantisation. Neural Networks 1, 303 (1988)
Kohonen, T.: Improved Versions of Learning Vector Quantization. In: IJCNN, vol. 1, pp. 545–550 (1990)
Kohonen, T.: Self-organised formation of topologically correct feature maps. In: Biological Cybernetics, vol. 43. Springer, Heilderberg (1982)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annual Eugenics 7(Part II), 179–188 (1936); also in Contributions to Mathematical Statistics. John Wiley, NY (1950)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis, p. 218. John Wiley & Sons, Chichester (1973)
Forina, M.: An Extendible Package for Data Exploration. In: Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Genoa, Italy
Horton, P., Nakai, K.: A Probablistic Classification System for Predicting the Cellular Localization Sites of Proteins. In: Intelligent Systems in Molecular Biology, pp. 109–115 (1996)
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Palmer-Brown, D., Draganova, C. (2010). Snap-Drift Self Organising Map. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_48
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DOI: https://doi.org/10.1007/978-3-642-15822-3_48
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