Advances in Self-Organizing Maps : 9th International Workshop, WSOM 2012 Santiago, Chile, December 12-14, 2012 Proceedings

Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more than 10,000 works have been based on SOMs. SOMs are unsupervised neural networks useful for clustering and visualization purposes. Many SOM applications have been developed in engineering and science,...

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Beste egile batzuk: Estévez, Pablo A. (Argitalpenaren zuzendaria), Principe, Jose C. (Argitalpenaren zuzendaria), Zegers, Pablo (Argitalpenaren zuzendaria)
Formatua: Livre numérique
Hizkuntza:Anglais
Argitaratua: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Edizioa:1st ed. 2013.
Saila:Advances in Intelligent Systems and Computing
Sarrera elektronikoa:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Oharra: Archives Springer e-books (Licence nationale)
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Edition sous un autre format:• Advances in Self-Organizing Maps, Texte imprimé, 9783642352294
• Advances in Self-Organizing Maps, Texte imprimé, 9783642352317
• Advances in Self-Organizing Maps, Texte imprimé, 9783642352294
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520 |a Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more than 10,000 works have been based on SOMs. SOMs are unsupervised neural networks useful for clustering and visualization purposes. Many SOM applications have been developed in engineering and science, and other fields.This book contains refereed papers presented at the 9th Workshop on Self-Organizing Maps (WSOM 2012) held at the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop brought together researchers and practitioners in the field of self-organizing systems. Among the book chapters there are excellent examples of the use of SOMs 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 SOMs as well as Learning Vector Quantization (LVQ) methods 
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