E-LIB Suche - Ergebnisse für: Villmann, T.

1

The coming of age of interpretable and explainable machine ..:

Lisboa, P.J.G. ; Saralajew, S. ; Vellido, A...
Neurocomputing. 535 (2023) - p. 25-39 , 2023

Link: https://doi.org/10.1016/..

2

A White-Box Workflow for the Prediction of Food Content Fro..:

, In: 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS),

Schubert, R. ; Reuss, L. V. ; Staps, D.... - p. 1-5 , 2023

Link: https://doi.org/10.1109/..

3

An Interpretable Two-Layered Neural Network Structure–Based..:

, In: Artificial Intelligence and Soft Computing; Lecture Notes in Computer Science,

Mohannazadeh Bakhtiari, M. ; Villmann, T. - p. 145-156 , 2023

Link: https://doi.org/10.1007/..

4

The Geometry of Decision Borders Between Affine Space Proto..:

, In: Artificial Intelligence and Soft Computing; Lecture Notes in Computer Science,

Mohannazadeh Bakhtiari, M. ; Villmann, A. ; Villmann, T. - p. 134-144 , 2023

Link: https://doi.org/10.1007/..

5

Learning vector quantization and relevances in complex coef..:

Straat, M. ; Kaden, M. ; Gay, M....
Neural Computing and Applications. 32 (2019) 24 - p. 18085-18099 , 2019

Link: https://doi.org/10.1007/..

6

Data dependent evaluation of dissimilarities in nearest pro..:

, In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM),

Kaden, M. ; Nebel, D. ; Melchert, F.... - p. 1-7 , 2017

Link: https://doi.org/10.1109/..

7

Types of (dis-)similarities and adaptive mixtures thereof f..:

Nebel, D. ; Kaden, M. ; Villmann, A..
Neurocomputing. 268 (2017) - p. 42-54 , 2017

Link: https://doi.org/10.1016/..

8

Learning vector quantization classifiers for ROC-optimizati..:

Villmann, T. ; Kaden, M. ; Hermann, W..
Computational Statistics. 33 (2016) 3 - p. 1173-1194 , 2016

Link: https://doi.org/10.1007/..

9

Learning matrix quantization and relevance learning based o..:

Bohnsack, A. ; Domaschke, K. ; Kaden, M...
Neurocomputing. 192 (2016) - p. 104-114 , 2016

Link: https://doi.org/10.1016/..

10

Lateral enhancement in adaptive metric learning for functio..:

Villmann, T. ; Kaden, M. ; Nebel, D..
Neurocomputing. 131 (2014) - p. 23-31 , 2014

Link: https://doi.org/10.1016/..

11

Aspects in Classification Learning - Review of Recent Devel..:

Kaden, M. ; Lange, M. ; Nebel, D....
Foundations of Computing and Decision Sciences. 39 (2014) 2 - p. 79-105 , 2014

Link: https://doi.org/10.2478/..

12

Divergence-based classification in learning vector quantiza..:

Mwebaze, E. ; Schneider, P. ; Schleif, F.-M....
Neurocomputing. 74 (2011) 9 - p. 1429-1435 , 2011

Link: https://doi.org/10.1016/..

13

Critical-illness-Myopathie und -Neuropathie (CRIMYN): Elekt..:

Baum, P. ; Bercker, S. ; Villmann, T...
Der Nervenarzt. 82 (2011) 4 - p. 468-474 , 2011

Link: https://doi.org/10.1007/..

14

Die Umsetzung des synergetischen Therapiemodells zur optima..:

Bergmann, B ; Villmann, T ; Gumz, A
PPmP - Psychotherapie · Psychosomatik · Medizinische Psychologie. 58 (2008) 2 - p. , 2008

Link: https://doi.org/10.1055/..

15

Prototype based fuzzy classification in clinical proteomics:

Schleif, F.-M. ; Villmann, T. ; Hammer, B.
International Journal of Approximate Reasoning. 47 (2008) 1 - p. 4-16 , 2008

Link: https://doi.org/10.1016/..