Thomas Villmann

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	All	Since 2016
Citations	6418	1826
h-index	37	22
i10-index	154	48

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Co-authors

Barbara HammerProfessor, Bielefeld UniversityVerified email at techfak.uni-bielefeld.de
Frank-Michael SchleifProfessor of Computational Intelligence, UAS Würzburg-SchweinfurtVerified email at fhws.de
Michael BiehlUniv. of Groningen, Bernoulli Inst. for Mathematics, Computer Science and Artificial IntelligenceVerified email at rug.nl
Marika KadenHochschule MittweidaVerified email at hs-mittweida.de
David NebelCI Group MittweidaVerified email at hs-mittweida.de
J. Michael HerrmannIPAB, School of Informatics, University of EdinburghVerified email at ed.ac.uk
Kerstin BunteRosalind Franklin Fellow at the University of GroningenVerified email at rug.nl
marie cottrellSAMM Université Paris 1 Panthéon-SorbonneVerified email at univ-paris1.fr
Thomas MartinetzProfessor of Computer Science, University of LübeckVerified email at inb.uni-luebeck.de
Swen HesseUniversität Leipzig, Molekulare Neurobildgebung IFB AdipositasErkrankungen, Klinik und PoliklinikVerified email at medizin.uni-leipzig.de
Reiner HauptFH NordhausenVerified email at fh-nordhausen.de
Jens Christian ClaussenLecturer, Dept. of Mathematics, Aston UniversityVerified email at aston.ac.uk
Sven HellbachWestsächsische Hochschule ZwickauVerified email at fh-zwickau.de
Ernest Tonny MwebazeResearch ScientistVerified email at sunbird.ai
Alessandro SperdutiUniversity of PadovaVerified email at math.unipd.it
Andreas SchierwagenProfessor, retired - Computer Science, Leipzig UniversityVerified email at informatik.uni-leipzig.de
Fabrice RossiProfessor of Data Science, CEREMADE, Université Paris Dauphine PSLVerified email at dauphine.psl.eu
Andreas KolbComputer Science, University of SiegenVerified email at uni-siegen.de

Thomas Villmann

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

Verified email at hs-mittweida.de

machine learning information theory pattern recognition learning vector quantization quantum computing


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Generalized relevance learning vector quantization B Hammer, T Villmann Neural Networks 15 (8-9), 1059-1068, 2002	494	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	405	1997
Neural maps in remote sensing image analysis T Villmann, E Merényi, B Hammer Neural Networks 16 (3-4), 389-403, 2003	212	2003
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	190	1997
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	175	2005
Supervised neural gas with general similarity measure B Hammer, M Strickert, T Villmann Neural Processing Letters 21 (1), 21-44, 2005	172	2005
Batch and median neural gas M Cottrell, B Hammer, A Hasenfuß, T Villmann Neural Networks 19 (6-7), 762-771, 2006	161	2006
Neural maps and topographic vector quantization HU Bauer, M Herrmann, T Villmann Neural networks 12 (4-5), 659-676, 1999	129	1999
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	109	2012
On the generalization ability of GRLVQ networks B Hammer, M Strickert, T Villmann Neural Processing Letters 21 (2), 109-120, 2005	97	2005
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	95	2010
Magnification control in self-organizing maps and neural gas T Villmann, JC Claussen Neural Computation 18 (2), 446-469, 2006	88	2006
Divergence-based vector quantization T Villmann, S Haase Neural Computation 23 (5), 1343-1392, 2011	87	2011
Prototype‐based models in machine learning M Biehl, B Hammer, T Villmann Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 2016	85	2016
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	73	2012
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	73	2006
Explicit magnification control of self-organizing maps for “forbidden” data E Merenyi, A Jain, T Villmann IEEE Transactions on Neural Networks 18 (3), 786-797, 2007	63	2007
Divergence-based classification in learning vector quantization E Mwebaze, P Schneider, FM Schleif, JR Aduwo, JA Quinn, S Haase, ... Neurocomputing 74 (9), 1429-1435, 2011	62	2011
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	60	2014
A new quantitative measure of topology preservation in Kohonen's feature maps T Villmann, R Der, T Martinetz Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN …, 1994	60	1994

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