By Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange (eds.)
The booklet collects the medical contributions provided on the tenth Workshop on Self-Organizing Maps (WSOM 2014) held on the collage 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 leads to the sphere of supervised and unsupervised vector quantization like self-organizing maps for facts mining and knowledge 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 techniques for category. The publication comprises the permitted papers of the workshop after a cautious assessment approach in addition to summaries of the invited talks. between those e-book chapters there are first-class examples of using self-organizing maps in agriculture, desktop technology, information visualization, wellbeing and fitness structures, economics, engineering, social sciences, textual content and photograph research and time sequence research. different chapters current the newest theoretical paintings on self-organizing maps in addition to studying vector quantization tools, comparable to touching on these how you can classical statistical determination methods.
All the contribution reveal that vector quantization equipment conceal a wide variety of program components together with info visualization of high-dimensional complicated info, complex choice making and category or facts clustering and information compression.
Read or Download Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014 PDF
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Extra resources for Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014
MS-SOM introduces a second level of organization of neurons following any magnitude function. This magnitude mechanism could be simplifying other types of biological processes as, for example, a magnitude derived from a chemical difussion map. This proposition is not supported by experimental biological proofs, as we know, but we considered interesting to develop a new method that, preserving the topological behaviour, added other levels of organization with certain biological plausibility. References 1.
In that respect, instead of a dynamic self-organizing map, we can obtain a dynamic k-means. References 1. : Can self-organization emerge through dynamic neural ﬁelds computation? Connection Science 23(1), 1–31 (2011) 2. : Dynamics of Pattern Formation in Lateral-Inhibition Type Neural Fields. Biological Cybernetics 27, 77–87 (1977) 34 J. Fix 3. : Building a mechanistic model of the development and function of the primary visual cortex. Journal of Physiology-Paris 106(5-6), 194–211 (2012) 4. : A neural ﬁeld model of the somatosensory cortex: formation, maintenance and reorganization of ordered topographic maps.
WSOM 2011. LNCS, vol. 6731, pp. 1–15. Springer, Heidelberg (2011) 20. : Topographic mapping of large dissimilarity data sets. 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.