Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

Alfredo Vellido

ISBN 10: 3030196410 / ISBN 13: 9783030196417

Verlag: Springer International Publishing, Springer International Publishing Apr 2019, 2019

Beschreibung

Neuware -This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), which was held in Barcelona, Spain, from the 26th to the 28th of June 2019. Since being founded in 1997, the conference has showcased the state of the art in unsupervised machine learning methods related to the successful and widely used self-organizing map (SOM) method, and extending its scope to clustering and data visualization. In this installment of the AISC series, the reader will find theoretical research on SOM, LVQ and related methods, as well as numerous applications to problems in fields ranging from business and engineering to the life sciences. Given the scope of its coverage, the book will be of interest to machine learning researchers and practitioners in general and, more specifically, to those looking for the latest developments in unsupervised learning and data visualization.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 356 pp. Englisch. Bestandsnummer des Verkäufers 9783030196417

Bibliografische Details

Titel: Advances in Self-Organizing Maps, Learning ...

Verlag: Springer International Publishing, Springer International Publishing Apr 2019

Erscheinungsdatum: 2019

Einband: Taschenbuch

Zustand: Neu

Über diesen Titel

Inhaltsangabe:

This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), which was held in Barcelona, Spain, from the 26th to the 28th of June 2019. Since being founded in 1997, the conference has showcased the state of the art in unsupervised machine learning methods related to the successful and widely used self-organizing map (SOM) method, and extending its scope to clustering and data visualization. In this installment of the AISC series, the reader will find theoretical research on SOM, LVQ and related methods, as well as numerous applications to problems in fields ranging from business and engineering to the life sciences. Given the scope of its coverage, the book will be of interest to machine learning researchers and practitioners in general and, more specifically, to those looking for the latest developments in unsupervised learning and data visualization.