Marika Kaden
Marika Kaden
Hochschule Mittweida
Verified email at
Cited by
Cited by
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
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
Functional relevance learning in generalized learning vector quantization
M Kästner, B Hammer, M Biehl, T Villmann
Neurocomputing 90, 85-95, 2012
Kernelized vector quantization in gradient-descent learning
T Villmann, S Haase, M Kaden
Neurocomputing 147, 83-95, 2015
A sparse kernelized matrix learning vector quantization model for human activity recognition.
M Kästner, M Strickert, T Villmann, SG Mittweida
ESANN, 2013
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
Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines
M Kaden, M Riedel, W Hermann, T Villmann
Soft Computing, 1-12, 2014
Self-Adjusting Reject Options in Prototype Based Classification
T Villmann, M Kaden, A Bohnsack, JM Villmann, T Drogies, S Saralajew, ...
Advances in Self-Organizing Maps and Learning Vector Quantization, 269-279, 2016
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
Application of an interpretable classification model on Early Folding Residues during protein folding
S Bittrich, M Kaden, C Leberecht, F Kaiser, T Villmann, D Labudde
BioData mining 12 (1), 1-16, 2019
Learning vector quantization classifiers for ROC-optimization
T Villmann, M Kaden, W Hermann, M Biehl
Computational Statistics 33 (3), 1173-1194, 2018
Gradient based learning in vector quantization using differentiable kernels
T Villmann, S Haase, M Kästner
Advances in Self-Organizing Maps, 193-204, 2013
Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks.
T Villmann, S Saralajew, A Villmann, M Kaden
IJCCI, 15-21, 2018
Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization.
M Kaden, W Hermann, T Villmann
ESANN, 2014
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
Generalized functional relevance learning vector quantization
M Kästner, B Hammer, M Biehl, T Villmann
Proceedings of the 19. European Symposium on Artificial Neural Networks …, 2011
Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014
T Villmann, FM Schleif, M Kaden, M Lange
Springer, 2014
Precision-Recall-Optimization in Learning vector quantization classifiers for improved medical classification systems
T Villmann, M Kaden, M Lange, P Stürmer, W Hermann
2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 71-77, 2014
Quantum-inspired learning vector quantizers for prototype-based classification
T Villmann, A Engelsberger, J Ravichandran, A Villmann, M Kaden
Neural Computing and Applications, 1-10, 2020
Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability
J Ravichandran, M Kaden, S Saralajew, T Villmann
Neurocomputing 403, 121-132, 2020
The system can't perform the operation now. Try again later.
Articles 1–20