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

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 latest ends up in the sector of supervised and unsupervised vector quantization like self-organizing maps for information 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 techniques for category. The booklet comprises the permitted papers of the workshop after a cautious evaluate strategy 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, desktop technology, info visualization, healthiness structures, economics, engineering, social sciences, textual content and photograph research and time sequence research. different chapters current the most recent theoretical paintings on self-organizing maps in addition to studying vector quantization tools, similar to referring to these the right way to classical statistical selection methods.

All the contribution reveal that vector quantization tools disguise a wide variety of program parts together with facts visualization of high-dimensional complicated information, complicated selection making and type or info clustering and knowledge compression.

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Additional 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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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. Neural Networks 19(6-7), 855–863 (2006) 11. : Batch and median neural gas. Neural Networks 19(6), 762–771 (2006) 12. : Self organizing map and symbolic data. Journal of Symbolic Data Analysis 2(1) (November 2004) 13.

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. MSSOM units tend to focus the learning process in data space zones with high values of a user-defined magnitude function. The model is based in two mechanisms: a secondary local competition step taking into account the magnitude of each unit, and the use of a learning factor, evaluated locally, for each unit.

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. 224228 25. : 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.

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