Advances in self-organizing maps and learning vector quantization [electronic resource] : proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 / Erzsébet Merényi, Michael J. Mendenhall, Patrick O'Driscoll, editors.
2016
QA76.87
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Title
Advances in self-organizing maps and learning vector quantization [electronic resource] : proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 / Erzsébet Merényi, Michael J. Mendenhall, Patrick O'Driscoll, editors.
ISBN
9783319285184 (electronic book)
3319285181 (electronic book)
9783319285177
3319285173
3319285181 (electronic book)
9783319285177
3319285173
Published
Cham : Springer, 2016.
Language
English
Description
1 online resource (xiii, 370 pages) : illustrations.
Item Number
10.1007/978-3-319-28518-4 doi
Call Number
QA76.87
Dewey Decimal Classification
006.32
Summary
This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization. The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.
Note
Includes author index.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 14, 2016).
Series
Advances in intelligent systems and computing ; v. 428.
Available in Other Form
Advances in self-organizing maps and learning vector quantization.
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Table of Contents
Self-Organizing Map Learning, Visualization, and Quality Assessment
Clustering and Time Series Analysis with Self-Organizing Maps and Neural Gas.-Applications in Control, Planning, and Dimensionality Reduction, and Hardware for Self-Organizing Maps
Self-Organizing Maps in Neuroscience and Medical Applications
Learning Vector Quantization Theories and Applications I
Learning Vector Quantization Theories and Applications II.
Clustering and Time Series Analysis with Self-Organizing Maps and Neural Gas.-Applications in Control, Planning, and Dimensionality Reduction, and Hardware for Self-Organizing Maps
Self-Organizing Maps in Neuroscience and Medical Applications
Learning Vector Quantization Theories and Applications I
Learning Vector Quantization Theories and Applications II.