Advances in Self-Organizing Maps and Learning Vector by Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy

By Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange (eds.)

The booklet collects the clinical contributions offered on the tenth Workshop on Self-Organizing Maps (WSOM 2014) held on the college of technologies Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. beginning with the 1st WSOM-workshop 1997 in Helsinki this workshop specializes in most modern ends up in the sector of supervised and unsupervised vector quantization like self-organizing maps for information mining and information classification.

This tenth WSOM introduced jointly greater than 50 researchers, specialists and practitioners within the attractive small city Mittweida in Saxony (Germany) within sight the mountains Erzgebirge to debate new advancements within the box of unsupervised self-organizing vector quantization platforms and studying vector quantization methods for class. The ebook includes the permitted papers of the workshop after a cautious evaluate procedure in addition to summaries of the invited talks. between those booklet chapters there are very good examples of using self-organizing maps in agriculture, desktop technology, facts visualization, wellbeing and fitness platforms, economics, engineering, social sciences, textual content and snapshot research and time sequence research. different chapters current the most recent theoretical paintings on self-organizing maps in addition to studying vector quantization tools, akin to pertaining to these the right way to classical statistical choice methods.

All the contribution exhibit that vector quantization equipment disguise a wide variety of program parts together with facts visualization of high-dimensional complicated info, complex determination making and class or information clustering and knowledge compression.

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Extra info for Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014

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How to make large self-organizing maps for nonvectorial data. Neural Networks 15(8), 945–952 (2002) 31. : The kernel self organising map. In: Proceedings of 4th International Conference on Knowledge-Based Intelligence Engineering Systems and Applied Technologies, pp. 317–320 (2000) 32. : Extending the SOM algorithm to non-euclidean distances via the kernel trick. K. ) ICONIP 2004. LNCS, vol. 3316, pp. 150–157. 1007/978-3-540-30499-9_22 33. : Modern hierarchical, agglomerative clustering algorithms.

In such a continuous stream of inputs, it is hard to figure out a mechanism that would reset the network to put it in a more favorable state. In J´er´emy Fix is in the IMS-MaLIS Research Group, Sup´elec (France). T. Villmann et al. 1007/978-3-319-07695-9_2, c Springer International Publishing Switzerland 2014 25 26 J. Fix this article, we advocate that such a mechanism is not necessary by introducing a dynamical neural network, relying on the dynamic neural field theory, able to reconsider its decision whenever it is no more appropriate.

Neural Computation 22(9), 2229–2284 (2010) 21. : Topographic processing of relational data. In: Proceedings of the 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld, Germany (September 2007) 22. : Nerf c-means: Non-euclidean relational fuzzy clustering. Pattern Recognition 27(3), 429–437 (1994) 22 F. Rossi 23. : Relational duals of the c-means clustering algorithms. Pattern Recognition 22(2), 205–212 (1989) 24. : A multilevel algorithm for partitioning graphs. In: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing (CDROM), Supercomputing 1995.

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