By Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange (eds.)
The booklet collects the clinical 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 ends up in the sector of supervised and unsupervised vector quantization like self-organizing maps for information mining and information 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 methods for class. The ebook includes the permitted papers of the workshop after a cautious evaluate procedure 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 technology, facts visualization, wellbeing and fitness platforms, economics, engineering, social sciences, textual content and snapshot research and time sequence research. different chapters current the most recent theoretical paintings on self-organizing maps in addition to studying vector quantization tools, akin to pertaining to these the right way to classical statistical choice methods.
All the contribution exhibit that vector quantization equipment disguise a wide variety of program parts together with facts visualization of high-dimensional complicated info, complex determination making and class or information clustering and knowledge 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
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In such a continuous stream of inputs, it is hard to ﬁgure out a mechanism that would reset the network to put it in a more favorable state. In J´er´emy Fix is in the IMS-MaLIS Research Group, Sup´elec (France). T. Villmann et al. 1007/978-3-319-07695-9_2, c Springer International Publishing Switzerland 2014 25 26 J. Fix this article, we advocate that such a mechanism is not necessary by introducing a dynamical neural network, relying on the dynamic neural ﬁeld theory, able to reconsider its decision whenever it is no more appropriate.
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