Thomas Villmann
Thomas Villmann
Professor for Mathematics and Computational Intelligence, University of Applied Sciences Mittweida
Verified email at hs-mittweida.de
Title
Cited by
Cited by
Year
Generalized relevance learning vector quantization
B Hammer, T Villmann
Neural Networks 15 (8-9), 1059-1068, 2002
4942002
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
4051997
Neural maps in remote sensing image analysis
T Villmann, E Merényi, B Hammer
Neural Networks 16 (3-4), 389-403, 2003
2122003
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
1901997
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
1752005
Supervised neural gas with general similarity measure
B Hammer, M Strickert, T Villmann
Neural Processing Letters 21 (1), 21-44, 2005
1722005
Batch and median neural gas
M Cottrell, B Hammer, A Hasenfuß, T Villmann
Neural Networks 19 (6-7), 762-771, 2006
1612006
Neural maps and topographic vector quantization
HU Bauer, M Herrmann, T Villmann
Neural networks 12 (4-5), 659-676, 1999
1291999
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
1092012
On the generalization ability of GRLVQ networks
B Hammer, M Strickert, T Villmann
Neural Processing Letters 21 (2), 109-120, 2005
972005
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
952010
Magnification control in self-organizing maps and neural gas
T Villmann, JC Claussen
Neural Computation 18 (2), 446-469, 2006
882006
Divergence-based vector quantization
T Villmann, S Haase
Neural Computation 23 (5), 1343-1392, 2011
872011
Prototype‐based models in machine learning
M Biehl, B Hammer, T Villmann
Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 2016
852016
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
732012
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
732006
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
632007
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
622011
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
602014
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
601994
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Articles 1–20