LEADER 04188nam 22005535 450 001 9910878981203321 005 20240802130253.0 010 $a3-031-67159-7 024 7 $a10.1007/978-3-031-67159-3 035 $a(MiAaPQ)EBC31579359 035 $a(Au-PeEL)EBL31579359 035 $a(CKB)33601175100041 035 $a(DE-He213)978-3-031-67159-3 035 $a(EXLCZ)9933601175100041 100 $a20240802d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond $eProceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10?12, 2024 /$fedited by Thomas Villmann, Marika Kaden, Tina Geweniger, Frank-Michael Schleif 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (240 pages) 225 1 $aLecture Notes in Networks and Systems,$x2367-3389 ;$v1087 311 $a3-031-67158-9 327 $aUnsupervised Learning-based Data Collection Planning with Dubins Vehicle and Constrained Data Retrieving Time -- Hyperbox GLVQ Based on Min Max Neurons -- Sparse clustering with K means which penalties and for which data -- Is t SNE Becoming the New Self organizing Map Similarities and Differences -- Pursuing the Perfect Projection A Projection Pursuit Framework for Deep Learning -- Generalizing self organizing maps large scale training of GMMs and applications in data science -- A Self Organizing UMAP For Clustering -- Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation -- Exploring data distributions in Machine Learning models with SOMs -- Interpretable Machine Learning in Endocrinology a Diagnostic Tool in Primary Aldosteronism -- The Beauty of Prototype Based Learning. 330 $aThe book presents the peer-reviewed contributions of the 15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM$+$ 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. 410 0$aLecture Notes in Networks and Systems,$x2367-3389 ;$v1087 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3 700 $aVillmann$b Thomas$01765025 701 $aKaden$b Marika$01765026 701 $aGeweniger$b Tina$01765027 701 $aSchleif$b Frank-Michael$01765028 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910878981203321 996 $aAdvances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond$94206264 997 $aUNINA