Thomas Villmann

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Barbara HammerProfessor, Bielefeld UniversityVerified email at techfak.uni-bielefeld.de
Frank-Michael SchleifProfessor of Computational Intelligence, Technical-UAS Würzburg-SchweinfurtVerified email at thws.de
Michael BiehlUniversity of Groningen, Bernoulli InstituteVerified email at rug.nl
Marika KadenHochschule MittweidaVerified email at hs-mittweida.de
David NebelCI Group MittweidaVerified email at hs-mittweida.de
Kerstin BunteRosalind Franklin Fellow at the University of GroningenVerified email at rug.nl
J. Michael HerrmannIPAB, School of Informatics, University of EdinburghVerified email at ed.ac.uk
Thomas MartinetzProfessor of Computer Science, University of LübeckVerified email at inb.uni-luebeck.de
marie cottrellSAMM Université Paris 1 Panthéon-SorbonneVerified email at univ-paris1.fr
Swen HesseUniversität Leipzig, Molekulare Neurobildgebung IFB AdipositasErkrankungen, Klinik und PoliklinikVerified email at medizin.uni-leipzig.de
Reiner HauptFH NordhausenVerified email at fh-nordhausen.de
Sven HellbachWestsächsische Hochschule ZwickauVerified email at fh-zwickau.de
Jens Christian ClaussenSenior Lecturer, University of BirminghamVerified email at bham.ac.uk
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	565	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	429	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	200	2005
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	200	1997
Batch and median neural gas M Cottrell, B Hammer, A Hasenfuß, T Villmann Neural Networks 19 (6-7), 762-771, 2006	197	2006
Supervised neural gas with general similarity measure B Hammer, M Strickert, T Villmann Neural Processing Letters 21, 21-44, 2005	192	2005
Prototype‐based models in machine learning M Biehl, B Hammer, T Villmann Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 2016	153	2016
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	142	2012
Neural maps and topographic vector quantization HU Bauer, M Herrmann, T Villmann Neural networks 12 (4-5), 659-676, 1999	137	1999
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
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	111	2012
On the generalization ability of GRLVQ networks B Hammer, M Strickert, T Villmann Neural Processing Letters 21, 109-120, 2005	107	2005
Divergence-based vector quantization T Villmann, S Haase Neural Computation 23 (5), 1343-1392, 2011	98	2011
Magnification control in self-organizing maps and neural gas T Villmann, JC Claussen Neural Computation 18 (2), 446-469, 2006	98	2006
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	86	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	80	2014
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	76	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	72	2014
Applications of the growing self-organizing map T Villmann, HU Bauer Neurocomputing 21 (1-3), 91-100, 1998	70	1998

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