Advances In Self Organizing Maps And Learning Vector Quantization
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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
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 and Learning Vector Quantization
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, Interpretable Machine Learning, and Beyond
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.