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
Thomas MartinetzProfessor of Computer Science, University of LübeckVerified email at inb.uni-luebeck.de
J. Michael HerrmannIPAB, School of Informatics, University of EdinburghVerified email at ed.ac.uk
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	576	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	437	1997
Neural maps in remote sensing image analysis T Villmann, E Merényi, B Hammer Neural Networks 16 (3-4), 389-403, 2003	235	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	205	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	201	1997
Batch and median neural gas M Cottrell, B Hammer, A Hasenfuß, T Villmann Neural Networks 19 (6-7), 762-771, 2006	196	2006
Supervised neural gas with general similarity measure B Hammer, M Strickert, T Villmann Neural Processing Letters 21, 21-44, 2005	196	2005
Prototype‐based models in machine learning M Biehl, B Hammer, T Villmann Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 2016	191	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	153	2012
Neural maps and topographic vector quantization HU Bauer, M Herrmann, T Villmann Neural networks 12 (4-5), 659-676, 1999	141	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	125	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	115	2010
On the generalization ability of GRLVQ networks B Hammer, M Strickert, T Villmann Neural Processing Letters 21, 109-120, 2005	114	2005
Divergence-based vector quantization T Villmann, S Haase Neural Computation 23 (5), 1343-1392, 2011	105	2011
Magnification control in self-organizing maps and neural gas T Villmann, JC Claussen Neural Computation 18 (2), 446-469, 2006	103	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
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	83	2023
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	78	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	76	2017

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