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

Name: Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond
Brand: Springer, Berlin
SKU: 9783031671586
Price: 845.73 PLN
Availability: InStock

Proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024

Autor: Thomas Villmann, Marika Kaden, Tina Geweniger, Frank-Michael Schleif

Język:

Oprawa: Miękka

Wydawca: Springer, Berlin

Dostępność: Dostępna u dostawcy

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845.73 zł

The book presents the peer-reviewed contributions of the 15th International Workshop on Self-Organiz...

Informacje o książce

Autor

Thomas Villmann, Marika Kaden, Tina Geweniger, Frank-Michael Schleif

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2024

strony

450

EAN

9783031671586

Enbook ID

46193469

Wydawca

Springer, Berlin

Waga

377

Wymiary

155 x 235

Pełny opis

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.

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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

Informacje o książce

Pełny opis

Możesz być zainteresowany

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