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

The booklet collects the clinical 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 recent ends up in 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 appealing small city Mittweida in Saxony (Germany) within reach the mountains Erzgebirge to debate new advancements within the box of unsupervised self-organizing vector quantization platforms and studying vector quantization ways for class. The e-book comprises the approved papers of the workshop after a cautious evaluate technique 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 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 equipment, reminiscent of bearing on these how you can classical statistical choice methods.

All the contribution exhibit that vector quantization tools conceal a wide variety of program components together with information visualization of high-dimensional advanced info, complex selection making and type or information clustering and information compression.

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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

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Brushing scatterplots. Technometrics 29(2), 127–142 (1987) 5. : Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing 71(7-9), 1257–1273 (2008) 6. : A maximum entropy approach to pairwise data clustering. In: Proceedings of the International Conference on Pattern Recognition, Hebrew University, Jerusalem, Israel, vol. II, pp. 207–212. IEEE Computer Society Press (1994) 7. : Speeding up the dissimilarity self-organizing maps by branch and bound. , Gra˜ na, M.

IEEE Computer Society Press (1994) 7. : Speeding up the dissimilarity self-organizing maps by branch and bound. , Gra˜ na, M. ) IWANN 2007. LNCS, vol. 4507, pp. 203–210. Springer, Heidelberg (2007) 8. : Acc´el´eration des cartes auto-organisatrices sur tableau de dissimilarit´es par s´eparation et ´evaluation. Revue des Nouvelles Technologies de l’Information, pp. 1–16 (June 2008), RNTI-C-2 Classification: points de vue crois´es. R´edacteurs invit´es : Mohamed Nadif et Fran¸cois-Xavier Jollois 9.

The parameter β we introduce in the equation facilitates the tuning 28 J. Fix (a) Random input (b) Structured input Fig. 1. A one-dimensional neural field with 50 positions is excited with randomly distributed inputs (a) or with more structured inputs (b). In both situations, we plot the input I(x, t) on the left, the U (x, t) membrane potential on the middle and the output firing rates f (V (x, t)) on the right. The membrane potentials U (x, t) have been saturated for the illustration to lie in [−3, 3] but they typically lie in a larger domain (see Fig.

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