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10th Workshop on Self-Organizing Maps

The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newe...

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Detalles Bibliográficos
Autores principales: Villmann, Thomas, Schleif, Frank-Michael, Kaden, Marika, Lange, Mandy
Lenguaje:eng
Publicado: Springer 2014
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-07695-9
http://cds.cern.ch/record/1742666
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author Villmann, Thomas
Schleif, Frank-Michael
Kaden, Marika
Lange, Mandy
author_facet Villmann, Thomas
Schleif, Frank-Michael
Kaden, Marika
Lange, Mandy
author_sort Villmann, Thomas
collection CERN
description The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification.   This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks.   Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods. All the contribution demonstrate that vector quantization methods cover a large range of application areas including data visualization of high-dimensional complex data, advanced decision making and classification or data clustering and data compression.
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spelling cern-17426662021-04-22T07:01:35Zdoi:10.1007/978-3-319-07695-9http://cds.cern.ch/record/1742666engVillmann, ThomasSchleif, Frank-MichaelKaden, MarikaLange, Mandy10th Workshop on Self-Organizing MapsEngineeringThe book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification.   This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks.   Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods. All the contribution demonstrate that vector quantization methods cover a large range of application areas including data visualization of high-dimensional complex data, advanced decision making and classification or data clustering and data compression.Springeroai:cds.cern.ch:17426662014
spellingShingle Engineering
Villmann, Thomas
Schleif, Frank-Michael
Kaden, Marika
Lange, Mandy
10th Workshop on Self-Organizing Maps
title 10th Workshop on Self-Organizing Maps
title_full 10th Workshop on Self-Organizing Maps
title_fullStr 10th Workshop on Self-Organizing Maps
title_full_unstemmed 10th Workshop on Self-Organizing Maps
title_short 10th Workshop on Self-Organizing Maps
title_sort 10th workshop on self-organizing maps
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-07695-9
http://cds.cern.ch/record/1742666
work_keys_str_mv AT villmannthomas 10thworkshoponselforganizingmaps
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