Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond: Proceedings of the 15th International ... (Lecture Notes in Networks and Systems, 1087) - Softcover

Softcover

ISBN 10: 3031671589 ISBN 13: 9783031671586

Publisher: Springer, 2024

Synopsis

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.

About this title

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2024
• Language: English
• ISBN 10: 3031671589
• ISBN 13: 9783031671586
• Binding: Paperback
• Number of pages: 241
• Editor: Villmann Thomas, Kaden Marika, Geweniger Tina, Schleif Frank-Michael