By Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange (eds.)
The publication collects the clinical contributions awarded 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 makes a speciality of most modern ends up in the sector of supervised and unsupervised vector quantization like self-organizing maps for info 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 sight the mountains Erzgebirge to debate new advancements within the box of unsupervised self-organizing vector quantization structures and studying vector quantization ways for category. The publication comprises the permitted papers of the workshop after a cautious evaluate procedure in addition to summaries of the invited talks. between those e-book chapters there are very good examples of using self-organizing maps in agriculture, computing device technological know-how, info visualization, healthiness platforms, economics, engineering, social sciences, textual content and picture research and time sequence research. different chapters current the newest theoretical paintings on self-organizing maps in addition to studying vector quantization equipment, comparable to touching on these ways to classical statistical determination methods.
All the contribution display that vector quantization equipment disguise a wide variety of software parts together with facts visualization of high-dimensional complicated info, complex selection making and class or information 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 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
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. : Dissimilarity clustering by hierarchical multi-level reﬁnement. In: Proceedings of the XXth European Symposium on Artiﬁcial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Bruges, Belgique, pp. 483–488 (March 2012) 10. : Fast algorithm and implementation of dissimilarity self-organizing maps.
1] for all of them. The fraction of samples placed in each cluster is approximately P/3. 05] and a learning factor that decreases exponentially with time. Three SOMs are initialized linearly in the data space using codebooks of 40, 80 and 160 units. Magnitude Sensitive Self-Organizing Maps 39 Fig. 1. Gaussian example. (a) D-matrix and Magnitude map of MS-SOM avoiding data mean (using M F4 as magnitude function). (b) Trained SOM. (c) MS-SOM trained with M F1 and (d) trained with M F4 . MS-SOMs have the same number of units than SOM (also uses 40, 80 and 160), uses the same initial codebooks, hji (t) and a value of β = 1.
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books). The MIT Press (2006) 10. : Homeostatic plasticity in neuronal networks: the more things change, the more they stay the same. Trends in Neurosciences 22(5), 221–227 (1999) 11. : A Mathematical Theory of the Functional Dynamics of Cortical and Thalamic Nervous Tissue. es/en Abstract. This paper presents a new neural algorithm, MS-SOM, as an extension of SOM, that maintaining the topological representation of stimulus also introduces a second level of organization of neurons.