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

The book "Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning" presents the peer-reviewed contributions of the 15th International Workshop on Self-Organizing Maps, Learning Vector Quantization, and Beyond (WSOM+ 2024), which took place at Mittweida University in Germany from July 10 to 12, 2024. It highlights new developments in the field of interpretable and explainable machine learning, particularly for classification tasks, data compression, and visualization. The focus is on prototype-based methods that are inherently interpretable, computationally efficient, and robust, making them preferred approaches for advanced machine learning tasks across a wide range of applications, from biomedicine to aerospace science, engineering, economics, and social sciences. The flexibility and simplicity of these approaches also allow for the integration of modern aspects such as deep architectures, probabilistic methods, and relevance learning. The book reflects both new theoretical aspects and interesting application cases in this research area.