Thomas Villmann

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Barbara HammerProfessor, Bielefeld UniversityEmail verificata su techfak.uni-bielefeld.de
Frank-Michael SchleifProfessor of Computational Intelligence, Technical-UAS Würzburg-SchweinfurtEmail verificata su thws.de
Michael BiehlUniversity of Groningen, Bernoulli InstituteEmail verificata su rug.nl
Marika KadenHochschule MittweidaEmail verificata su hs-mittweida.de
David NebelCI Group MittweidaEmail verificata su hs-mittweida.de
Kerstin BunteRosalind Franklin Fellow at the University of GroningenEmail verificata su rug.nl
J. Michael HerrmannIPAB, School of Informatics, University of EdinburghEmail verificata su ed.ac.uk
Thomas MartinetzProfessor of Computer Science, University of LübeckEmail verificata su inb.uni-luebeck.de
marie cottrellSAMM Université Paris 1 Panthéon-SorbonneEmail verificata su univ-paris1.fr
Swen HesseUniversität Leipzig, Molekulare Neurobildgebung IFB AdipositasErkrankungen, Klinik und PoliklinikEmail verificata su medizin.uni-leipzig.de
Reiner HauptFH NordhausenEmail verificata su fh-nordhausen.de
Sven HellbachWestsächsische Hochschule ZwickauEmail verificata su fh-zwickau.de
Jens Christian ClaussenSenior Lecturer, University of BirminghamEmail verificata su bham.ac.uk
Ernest Tonny MwebazeResearch ScientistEmail verificata su sunbird.ai
Alessandro SperdutiUniversity of PadovaEmail verificata su math.unipd.it
Andreas SchierwagenProfessor, retired - Computer Science, Leipzig UniversityEmail verificata su informatik.uni-leipzig.de
Fabrice RossiProfessor of Data Science, CEREMADE, Université Paris Dauphine PSLEmail verificata su dauphine.psl.eu
Andreas KolbComputer Science, University of SiegenEmail verificata su uni-siegen.de

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Thomas Villmann

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

Email verificata su hs-mittweida.de

machine learning information theory pattern recognition learning vector quantization quantum computing


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Generalized relevance learning vector quantization B Hammer, T Villmann Neural Networks 15 (8-9), 1059-1068, 2002	564	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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