Advances in Self-organizing Maps, Learning Vector Quantization, Interpretable Machine Learning and Beyond: Proceedings of the 15th International Workshop, Wsom+ 2024, Mittweida, Germany, July 10 12, 2024

Villmann, Thomas (Editor)/ Kaden, Marika (Editor)/ Geweniger, Tina (Editor)/ Schleif, Frank-michael (Editor)

ISBN 10: 3031671589 / ISBN 13: 9783031671586

Editore: Springer-Nature New York Inc, 2024

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241 pages. 9.25x6.10x9.21 inches. In Stock.

Codice articolo x-3031671589

Dati bibliografici

• Titolo: Advances in Self-organizing Maps, Learning Vector Quantization, Interpretable Machine Learning and Beyond: Proceedings of the 15th International Workshop, Wsom+ 2024, Mittweida, Germany, July 10 12, 2024
• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031671589
• ISBN 13: 9783031671586
• Rilegatura: Paperback
• Condizione: Brand New

Descrizione articolo

Riassunto

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.