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Advances In Selforganizing Maps Learning Vector Quantization Clustering And Data Visualization Proceedings Of The 13th International Workshop Wsom 2019 Barcelona Spain June 2628 2019 1st Ed Alfredo Vellido

  • SKU: BELL-10488160
Advances In Selforganizing Maps Learning Vector Quantization Clustering And Data Visualization Proceedings Of The 13th International Workshop Wsom 2019 Barcelona Spain June 2628 2019 1st Ed Alfredo Vellido
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Advances In Selforganizing Maps Learning Vector Quantization Clustering And Data Visualization Proceedings Of The 13th International Workshop Wsom 2019 Barcelona Spain June 2628 2019 1st Ed Alfredo Vellido instant download after payment.

Publisher: Springer International Publishing
File Extension: PDF
File size: 45.13 MB
Author: Alfredo Vellido, Karina Gibert, Cecilio Angulo, José David Martín Guerrero
ISBN: 9783030196424, 3030196429
Language: English
Year: 2020
Edition: 1st ed.

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Advances In Selforganizing Maps Learning Vector Quantization Clustering And Data Visualization Proceedings Of The 13th International Workshop Wsom 2019 Barcelona Spain June 2628 2019 1st Ed Alfredo Vellido by Alfredo Vellido, Karina Gibert, Cecilio Angulo, José David Martín Guerrero 9783030196424, 3030196429 instant download after payment.

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.

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