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ORCID15th WSOM+ 2024: Mittweida, Germany
- Thomas Villmann, Marika Kaden, Tina Geweniger, Frank-Michael Schleif:
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. Lecture Notes in Networks and Systems 1087, Springer 2024, ISBN 978-3-031-67158-6 - Rewbenio A. Frota
, Guilherme A. Barreto, Marley M. B. R. Vellasco, Candida Menezes de Jesus:
New Cloth Unto an Old Garment: SOM for Regeneration Learning. 1-10 - Jindriska Deckerová, Jan Faigl:
Unsupervised Learning-Based Data Collection Planning with Dubins Vehicle and Constrained Data Retrieving Time. 11-21 - Thomas Villmann, T. Davies, Alexander Engelsberger:
Hyperbox-GLVQ Based on Min-Max-Neurons. 22-31 - Marie Chavent, Marie Cottrell, Alex Mourer, Madalina Olteanu:
Sparse Clustering with K-Means - Which Penalties and for Which Data? 32-41 - John A. Lee:
Is t-SNE Becoming the New Self-organizing Map? Similarities and Differences. 42 - Jan-Ole Perschewski, Johann Schmidt, Sebastian Stober:
Pursuing the Perfect Projection: A Projection Pursuit Framework for Deep Learning. 43-52 - Alexander Gepperth:
Generalizing Self-organizing Maps: Large-Scale Training of GMMs and Applications in Data Science. 53-62 - Josh Taylor, Stella S. R. Offner:
A Self-Organizing UMAP for Clustering. 63-73 - Marika Kaden, Julius Voigt, Katrin Sophie Bohnsack, Mandy Lange-Geisler, Thomas Villmann:
Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation. 74-85 - Caroline König, Alfredo Vellido:
Exploring Data Distributions in Machine Learning Models with SOMs. 86-95 - Michael Biehl, David Pavlov, Alice J. Sitch, Alessandro Prete, Wiebke Arlt:
Interpretable Machine Learning in Endocrinology: A Diagnostic Tool in Primary Aldosteronism. 96-105 - Peter Tino:
The Beauty of Prototype Based Learning. 106 - Josh Taylor, Stella S. R. Offner:
Setting Vector Quantizer Resolution via Density Estimation Theory. 107-117 - Frank-Michael Schleif:
Practical Approaches to Approximate Dominant Eigenvalues in Large Matrices. 118-128 - Jean-Charles Lamirel:
Enhancing LDA Method by the Use of Feature Maximization. 129-138 - Barbara Hammer:
Explaining Neural Networks - Deep and Shallow. 139-140 - Felix Störck, Fabian Hinder, Johannes Brinkrolf, Benjamin Paassen, Valerie Vaquet, Barbara Hammer:
FairGLVQ: Fairness in Partition-Based Classification. 141-151 - Ronny Schubert, Thomas Villmann:
About Interpretable Learning Rules for Vector Quantizers - A Methodological Approach. 152-162 - Lydia Fischer, Patricia Wollstadt:
Precision and Recall Reject Curves. 163-173 - Daniel Staps, Thomas Villmann, Benjamin Paaßen:
K Minimum Enclosing Balls for Outlier Detection. 174-184 - Alexander Gepperth:
Probabilistic Models with Invariance. 185-195 - Bangguo Xu, Simei Yan, Liang Liu, Frank-Michael Schleif:
Optimizing YOLOv5 for Green AI: A Study on Model Pruning and Lightweight Networks. 196-205 - Christian W. Frey:
Process Phase Monitoring in Industrial Manufacturing Processes with a Hybrid Unsupervised Learning Strategy. 206-215 - Steven Lehmann, Jörg Schließer, Sandra Schumann, Heiner Winkler, Iren Jabs:
Knowledge Management in SMEs: Applying Link Prediction for Assisted Decision Making. 216-225
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