Advances in Self-Organizing Maps and Learning Vector Quantization

Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016

Erzsébet Merényi, Michael J. Mendenhall, Patrick O'Driscoll (Herausgeber)

Buch | Softcover

XIII, 370 Seiten

2016 | 1st ed. 2016
Springer International Publishing (Verlag)
978-3-319-28517-7 (ISBN)

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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.

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.

Erscheinungsdatum	08.10.2016
Reihe/Serie	Advances in Intelligent Systems and Computing
Zusatzinfo	XIII, 370 p. 89 illus., 65 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik
Schlagworte	artificial intelligence (incl. robotics) • Computational Intelligence • Engineering • Intelligent Systems • Learning Vector Quantization • LVQ • Self-Organizing Maps • SOM • WSOM 2016
ISBN-10	3-319-28517-3 / 3319285173
ISBN-13	978-3-319-28517-7 / 9783319285177
Zustand	Neuware