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

Author: Alfredo Vellido

Publisher: Springer

Published: 2019-04-27

Total Pages: 342

ISBN-13: 3030196429

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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.

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

Author: Jan Faigl

Publisher: Springer Nature

Published: 2022-08-26

Total Pages: 130

ISBN-13: 3031154444

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In this collection, the reader can find recent advancements in self-organizing maps (SOMs) and learning vector quantization (LVQ), including progressive ideas on exploiting features of parallel computing. The collection is balanced in presenting novel theoretical contributions with applied results in traditional fields of SOMs, such as visualization problems and data analysis. Besides, the collection further includes less traditional deployments in trajectory clustering and recent results on exploiting quantum computation. The presented book is worth interest to data analysis and machine learning researchers and practitioners, specifically those interested in being updated with current developments in unsupervised learning, data visualization, and self-organization.

Advances in Self-Organizing Maps and Learning Vector Quantization
Advances in Self-Organizing Maps and Learning Vector Quantization

Author: Thomas Villmann

Publisher: Springer

Published: 2014-06-10

Total Pages: 314

ISBN-13: 3319076957

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The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification. This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks. Among these book chapters there are excellent examples of the use of self-organizing maps 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 self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods. All the contribution demonstrate that vector quantization methods cover a large range of application areas including data visualization of high-dimensional complex data, advanced decision making and classification or data clustering and data compression.

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

Author: Jan Faigl

Publisher:

Published: 2022

Total Pages: 0

ISBN-13: 9783031154454

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In this collection, the reader can find recent advancements in self-organizing maps (SOMs) and learning vector quantization (LVQ), including progressive ideas on exploiting features of parallel computing. The collection is balanced in presenting novel theoretical contributions with applied results in traditional fields of SOMs, such as visualization problems and data analysis. Besides, the collection further includes less traditional deployments in trajectory clustering and recent results on exploiting quantum computation. The presented book is worth interest to data analysis and machine learning researchers and practitioners, specifically those interested in being updated with current developments in unsupervised learning, data visualization, and self-organization.

Advances in Self-Organizing Maps and Learning Vector Quantization
Advances in Self-Organizing Maps and Learning Vector Quantization

Author: Erzsébet Merényi

Publisher: Springer

Published: 2016-01-07

Total Pages: 353

ISBN-13: 3319285181

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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.

Advances in Self-Organizing Maps
Advances in Self-Organizing Maps

Author: Pablo A. Estévez

Publisher: Springer Science & Business Media

Published: 2012-12-14

Total Pages: 371

ISBN-13: 3642352308

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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.

Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond
Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond

Author: Thomas Villmann

Publisher: Springer

Published: 2024-08-30

Total Pages: 0

ISBN-13: 9783031671586

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The book presents the peer-reviewed contributions of the 15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM$+$ 2024), held at the University of Applied Sciences Mittweida (UAS Mitt\-weida), Germany, on July 10–12, 2024. The book highlights new developments in the field of interpretable and explainable machine learning for classification tasks, data compression and visualization. Thereby, the main focus is on prototype-based methods with inherent interpretability, computational sparseness and robustness making them as favorite methods for advanced machine learning tasks in a wide variety of applications ranging from biomedicine, space science, engineering to economics and social sciences, for example. The flexibility and simplicity of those approaches also allow the integration of modern aspects such as deep architectures, probabilistic methods and reasoning as well as relevance learning. The book reflects both new theoretical aspects in this research area and interesting application cases. Thus, this book is recommended for researchers and practitioners in data analytics and machine learning, especially those who are interested in the latest developments in interpretable and robust unsupervised learning, data visualization, classification and self-organization.

Advances in Self-Organizing Maps
Advances in Self-Organizing Maps

Author: J.C. Principe

Publisher: Springer

Published: 2009-06-04

Total Pages: 383

ISBN-13: 3642023975

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th These proceedings contain refereed papers presented at the 7 WSOM held at the Casa Monica Hotel, St. Augustine, Florida, June 8–10, 2009. We designed the wo- shop to serve as a regular forum for researchers in academia and industry who are interested in the exciting field of self-organizing maps (SOM). The program includes excellent examples of the use of SOM in many areas of social sciences, economics, computational biology, engineering, time series analysis, data visualization and c- puter science as well a vibrant set of theoretical papers that keep pushing the envelope of the original SOM. Our deep appreciation is extended to Teuvo Kohonen and Ping Li for the plenary talks and Amaury Lendasse for the organization of the special sessions. Our sincere thanks go to the members of the Technical Committee and other reviewers for their excellent and timely reviews, and above all to the authors whose contributions made this workshop possible. Special thanks go to Julie Veal for her dedication and hard work in coordinating the many details necessary to put together the program and local arrangements. Jose C. Principe Risto Miikkulainen