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David Nebel
David Nebel
CI Group Mittweida
Verified email at hs-mittweida.de
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Cited by
Year
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
652014
Types of (dis-) similarities and adaptive mixtures thereof for improved classification learning
D Nebel, M Kaden, A Villmann, T Villmann
Neurocomputing 268, 42-54, 2017
282017
Median variants of learning vector quantization for learning of dissimilarity data
D Nebel, B Hammer, K Frohberg, T Villmann
Neurocomputing 169, 295-305, 2015
252015
Generative versus discriminative prototype based classification
B Hammer, D Nebel, M Riedel, T Villmann
Advances in Self-Organizing Maps and Learning Vector Quantization, 123-132, 2014
202014
About learning of supervised generative models for dissimilarity data
D Nebel, B Hammer, T Villmann
Machine Learning Reports 7, 1-19, 2013
182013
A median variant of generalized learning vector quantization
D Nebel, B Hammer, T Villmann
International Conference on Neural Information Processing, 19-26, 2013
182013
Differentiable kernels in generalized matrix learning vector quantization
M Kästner, D Nebel, M Riedel, M Biehl, T Villmann
2012 11th International Conference on Machine Learning and Applications 1 …, 2012
162012
Rejection strategies for learning vector quantization–a comparison of probabilistic and deterministic approaches
L Fischer, D Nebel, T Villmann, B Hammer, H Wersing
Advances in Self-Organizing Maps and Learning Vector Quantization, 109-118, 2014
152014
Investigation of activation functions for generalized learning vector quantization
T Villmann, J Ravichandran, A Villmann, D Nebel, M Kaden
International Workshop on Self-Organizing Maps, 179-188, 2019
142019
Supervised Generative Models for Learning Dissimilarity Data.
D Nebel, B Hammer, T Villmann
ESANN, 2014
102014
Adaptive Hausdorff distances and tangent distance adaptation for transformation invariant classification learning
S Saralajew, D Nebel, T Villmann
International Conference on Neural Information Processing, 362-371, 2016
92016
Learning vector quantization with adaptive cost-based outlier-rejection
T Villmann, M Kaden, D Nebel, M Biehl
International Conference on Computer Analysis of Images and Patterns, 772-782, 2015
92015
Building the library of RNA 3D nucleotide conformations using the clustering approach
T Zok, M Antczak, M Riedel, D Nebel, T Villmann, P Lukasiak, J Blazewicz, ...
International Journal of Applied Mathematics and Computer Science 25 (3 …, 2015
92015
ICMLA Face Recognition Challenge--Results of the Team Computational Intelligence Mittweida
T Villmann, M Kästner, D Nebel, M Riedel
2012 11th International Conference on Machine Learning and Applications 2 …, 2012
92012
Similarities, dissimilarities and types of inner products for data analysis in the context of machine learning
T Villmann, M Kaden, D Nebel, A Bohnsack
International Conference on Artificial Intelligence and Soft Computing, 125-133, 2016
62016
Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities.
M Kaden, D Nebel, T Villmann, M Verleysen
ESANN, 2016
62016
Activation functions for generalized learning vector quantization-a performance comparison
T Villmann, J Ravichandran, A Villmann, D Nebel, M Kaden
arXiv preprint arXiv:1901.05995, 2019
42019
Lateral enhancement in adaptive metric learning for functional data
T Villmann, M Kaden, D Nebel, M Riedel
Neurocomputing 131, 23-31, 2014
42014
Median variants of LVQ for optimization of statistical quality measures for classification of dissimilarity data
D Nebel, T Villmann
Machine Learning Reports 8 (MLR-03-2014), 1-25, 2014
32014
Non-Euclidean principal component analysis for matrices by Hebbian learning
M Lange, D Nebel, T Villmann
International Conference on Artificial Intelligence and Soft Computing, 77-88, 2014
32014
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