By Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange (eds.)
The booklet collects the medical 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 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 type. The ebook includes the permitted papers of the workshop after a cautious overview method in addition to summaries of the invited talks. between those publication chapters there are first-class examples of using self-organizing maps in agriculture, computing device technological know-how, information visualization, health and wellbeing 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, akin to bearing on these the right way to classical statistical determination methods.
All the contribution show that vector quantization tools hide a wide variety of program parts together with info visualization of high-dimensional advanced info, complicated choice making and category or info 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
A comparison between dissimilarity som and kernel som for clustering the vertices of a graph. In: Proceedings of the 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld, Germany (September 2007) 50. : Using the nystr¨ om method to speed up kernel machines. fr Abstract. In this paper, an original dynamical system derived from dynamic neural ﬁelds is studied in the context of the formation of topographic maps. This dynamical system overcomes limitations of the original Self-Organizing Map (SOM) model of Kohonen.
B) 3D depth image. (c) Curvature map applying Canny. Zones with higher curvature are clearer. (d) Final surface models after training a SOM and (e) a MS-SOM following curvature. its centroid density is higher in the decision regions, giving as result a better performance in the classiﬁcation task. Figure 2 shows a MS-SOM trained with the iris dataset: (a) Map with colours depending on the classes for each unit (interpolating colours mean that a unit has samples from diﬀerent classes), (b) map with the ﬁnal assigned class for each unit, and (c) magnitude associated to each unit.
Error potentials for self-organization. In: Proceedings of 1993 IEEE International Conference on Neural Networks (Joint FUZZ-IEEE 1993 and ICNN 1993 [IJCNN 1993]), vol. III, pp. 1219–1223. IEEE/INNS, San Francisco (1993) 26. : Pairwise data clustering by deterministic annealing. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(1), 1–14 (1997) 27. : Self-organizing maps of symbol strings. Technical report A42, Laboratory of computer and information science, Helsinki University of Technology, Finland (1996) 28.