Prototype-based models in machine learning von Biehl, Michael, Hammer, Barbara, Villmann, Thomas

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Quantum Computing Approaches for Vector Quantization-Current Perspectives and Developments von Engelsberger, Alexander, Villmann, Thomas

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Divergence-Based Vector Quantization von Villmann, Thomas, Haase, Sven

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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, G... von Villmann, Thomas, Kaden, Marika, Geweniger, Tina, Schleif, Frank-Michael

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AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task von Zoghlami, Feryel, Kaden, Marika, Villmann, Thomas, Schneider, Germar, Heinrich, Harald

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Generalized relevance learning vector quantization von Hammer, Barbara, Villmann, Thomas

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Magnification Control in Self-Organizing Maps and Neural Gas von Villmann, Thomas, Claussen, Jens Christian

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The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers von Bohnsack, Katrin Sophie, Kaden, Marika, Abel, Julia, Saralajew, Sascha, Villmann, Thomas

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Batch and median neural gas von Cottrell, Marie, Hammer, Barbara, Hasenfuß, Alexander, Villmann, Thomas

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Neural maps in remote sensing image analysis von Villmann, Thomas, Merényi, Erzsébet, Hammer, Barbara

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Application of an interpretable classification model on Early Folding Residues during protein folding von Bittrich, Sebastian, Kaden, Marika, Leberecht, Christoph, Kaiser, Florian, Villmann, Thomas, Labudde, Dirk

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Variants of recurrent learning vector quantization von Ravichandran, Jensun, Kaden, Marika, Villmann, Thomas

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Building the library of RNA 3D nucleotide conformations using the clustering approach von Zok, Tomasz, Antczak, Maciej, Riedel, Martin, Nebel, David, Villmann, Thomas, Lukasiak, Piotr, Blazewicz, Jacek, Szachniuk, Marta

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Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective von Bohnsack, Katrin Sophie, Kaden, Marika, Abel, Julia, Villmann, Thomas

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Quantum-inspired learning vector quantizers for prototype-based classification: Confidential: for personal use only—submitted to Neural Networks and Applications 5/2020 von Villmann, Thomas, Engelsberger, Alexander, Ravichandran, Jensun, Villmann, Andrea, Kaden, Marika

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On the generalization ability of GRLVQ networks von HAMMER, Barbara, STRICKERT, Marc, VILLMANN, Thomas

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Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability von Ravichandran, Jensun, Kaden, Marika, Saralajew, Sascha, Villmann, Thomas

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Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters von Lange, Mandy, Fischer, Lydia, Kaden, Marika, Villmann, Thomas, Geweniger, Tina

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Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences von Bunte, Kerstin, Haase, Sven, Biehl, Michael, Villmann, Thomas

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Kernelized vector quantization in gradient-descent learning von Villmann, Thomas, Haase, Sven, Kaden, Marika

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