Thomas Villmann
Thomas Villmann
Professor for Mathematics and Computational Intelligence, University of Applied Sciences Mittweida
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Cited by
Cited by
Generalized relevance learning vector quantization
B Hammer, T Villmann
Neural Networks 15 (8-9), 1059-1068, 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
Neural maps in remote sensing image analysis
T Villmann, E Merényi, B Hammer
Neural Networks 16 (3-4), 389-403, 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
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
Batch and median neural gas
M Cottrell, B Hammer, A Hasenfuß, T Villmann
Neural Networks 19 (6-7), 762-771, 2006
Supervised neural gas with general similarity measure
B Hammer, M Strickert, T Villmann
Neural Processing Letters 21, 21-44, 2005
Prototype‐based models in machine learning
M Biehl, B Hammer, T Villmann
Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 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
Neural maps and topographic vector quantization
HU Bauer, M Herrmann, T Villmann
Neural networks 12 (4-5), 659-676, 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
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
On the generalization ability of GRLVQ networks
B Hammer, M Strickert, T Villmann
Neural Processing Letters 21, 109-120, 2005
Divergence-based vector quantization
T Villmann, S Haase
Neural Computation 23 (5), 1343-1392, 2011
Magnification control in self-organizing maps and neural gas
T Villmann, JC Claussen
Neural Computation 18 (2), 446-469, 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
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
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
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
Exploratory observation machine (XOM) with Kullback-Leibler divergence for dimensionality reduction and visualization
K Bunte, B Hammer, T Villmann, M Biehl, A Wismüller
18th European Symposium on Artificial Neural Networks (ESANN 2010), 87-92, 2010
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