Thomas Villmann

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Barbara HammerProfessor, Bielefeld UniversityAdresse e-mail validée de techfak.uni-bielefeld.de
Frank-Michael SchleifProfessor of Computational Intelligence, Technical-UAS Würzburg-SchweinfurtAdresse e-mail validée de thws.de
Michael BiehlUniversity of Groningen, Bernoulli InstituteAdresse e-mail validée de rug.nl
Marika KadenHochschule MittweidaAdresse e-mail validée de hs-mittweida.de
David NebelCI Group MittweidaAdresse e-mail validée de hs-mittweida.de
Kerstin BunteRosalind Franklin Fellow at the University of GroningenAdresse e-mail validée de rug.nl
Thomas MartinetzProfessor of Computer Science, University of LübeckAdresse e-mail validée de inb.uni-luebeck.de
J. Michael HerrmannIPAB, School of Informatics, University of EdinburghAdresse e-mail validée de ed.ac.uk
Swen HesseUniversität Leipzig, Molekulare Neurobildgebung IFB AdipositasErkrankungen, Klinik und PoliklinikAdresse e-mail validée de medizin.uni-leipzig.de
marie cottrellSAMM Université Paris 1 Panthéon-SorbonneAdresse e-mail validée de univ-paris1.fr
Reiner HauptFH NordhausenAdresse e-mail validée de fh-nordhausen.de
Sven HellbachWestsächsische Hochschule ZwickauAdresse e-mail validée de fh-zwickau.de
Jens Christian ClaussenSenior Lecturer, University of BirminghamAdresse e-mail validée de bham.ac.uk
Ernest Tonny MwebazeResearch ScientistAdresse e-mail validée de sunbird.ai
Alessandro SperdutiUniversity of PadovaAdresse e-mail validée de math.unipd.it
Andreas SchierwagenProfessor, retired - Computer Science, Leipzig UniversityAdresse e-mail validée de informatik.uni-leipzig.de
Fabrice RossiProfessor of Data Science, CEREMADE, Université Paris Dauphine PSLAdresse e-mail validée de dauphine.psl.eu
Andreas KolbComputer Science, University of SiegenAdresse e-mail validée de uni-siegen.de

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Thomas Villmann

Professor for Mathematics and Computational Intelligence, University of Applied Sciences Mittweida

Adresse e-mail validée de hs-mittweida.de

machine learning information theory pattern recognition learning vector quantization quantum computing


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Generalized relevance learning vector quantization B Hammer, T Villmann Neural Networks 15 (8-9), 1059-1068, 2002	582	2002
Topology preservation in self-organizing feature maps: exact definition and measurement T Villmann, R Der, M Herrmann, TM Martinetz IEEE transactions on neural networks 8 (2), 256-266, 1997	441	1997
Neural maps in remote sensing image analysis T Villmann, E Merényi, B Hammer Neural Networks 16 (3-4), 389-403, 2003	238	2003
Serotonin and dopamine transporter imaging in patients with obsessive–compulsive disorder S Hesse, U Müller, T Lincke, H Barthel, T Villmann, MC Angermeyer, ... Psychiatry Research: Neuroimaging 140 (1), 63-72, 2005	211	2005
Prototype‐based models in machine learning M Biehl, B Hammer, T Villmann Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 2016	206	2016
Growing a hypercubical output space in a self-organizing feature map HU Bauer, T Villmann IEEE transactions on neural networks 8 (2), 218-226, 1997	202	1997
Supervised neural gas with general similarity measure B Hammer, M Strickert, T Villmann Neural Processing Letters 21, 21-44, 2005	198	2005
Batch and median neural gas M Cottrell, B Hammer, A Hasenfuß, T Villmann Neural Networks 19 (6-7), 762-771, 2006	197	2006
Limited rank matrix learning, discriminative dimension reduction and visualization K Bunte, P Schneider, B Hammer, FM Schleif, T Villmann, M Biehl Neural Networks 26, 159-173, 2012	155	2012
Neural maps and topographic vector quantization HU Bauer, M Herrmann, T Villmann Neural networks 12 (4-5), 659-676, 1999	143	1999
Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences K Bunte, S Haase, M Biehl, T Villmann Neurocomputing 90, 23-45, 2012	130	2012
Regularization in matrix relevance learning P Schneider, K Bunte, H Stiekema, B Hammer, T Villmann, M Biehl IEEE Transactions on Neural Networks 21 (5), 831-840, 2010	116	2010
On the generalization ability of GRLVQ networks B Hammer, M Strickert, T Villmann Neural Processing Letters 21, 109-120, 2005	116	2005
Divergence-based vector quantization T Villmann, S Haase Neural Computation 23 (5), 1343-1392, 2011	106	2011
Magnification control in self-organizing maps and neural gas T Villmann, JC Claussen Neural Computation 18 (2), 446-469, 2006	103	2006
The coming of age of interpretable and explainable machine learning models PJG Lisboa, S Saralajew, A Vellido, R Fernández-Domenech, T Villmann Neurocomputing 535, 25-39, 2023	102	2023
Computational aspects of inverse analyses for determining softening curves of concrete V Slowik, B Villmann, N Bretschneider, T Villmann Computer Methods in Applied Mechanics and Engineering 195 (52), 7223-7236, 2006	87	2006
Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. M Lange, D Zühlke, O Holz, T Villmann, SG Mittweida ESANN, 271-276, 2014	81	2014
Aspects in classification learning-Review of recent developments in Learning Vector Quantization M Kaden, M Lange, D Nebel, M Riedel, T Geweniger, T Villmann Foundations of Computing and Decision Sciences 39 (2), 79-105, 2014	80	2014
Can learning vector quantization be an alternative to svm and deep learning?-Recent trends and advanced variants of learning vector quantization for classification learning T Villmann, A Bohnsack, M Kaden Journal of Artificial Intelligence and Soft Computing Research 7 (1), 65-81, 2017	77	2017

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