David Nebel

According to our database, David Nebel authored at least 21 papers between 2012 and 2019.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2019

Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison.

Thomas Villmann, John Ravichandran, Andrea Villmann, David Nebel, Marika Kaden

CoRR, 2019

Investigation of Activation Functions for Generalized Learning Vector Quantization.

Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden

Proceedings of the Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, 2019

2017

Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning.

David Nebel, Marika Kaden, Andrea Villmann, Thomas Villmann

Neurocomputing, 2017

Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities.

Marika Kaden, David Nebel, Friedrich Melchert, Andreas Backhaus, Udo Seiffert, Thomas Villmann

Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 2017

2016

Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization.

David Nebel, Thomas Villmann

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2016

Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning.

Sascha Saralajew, David Nebel, Thomas Villmann

Proceedings of the Neural Information Processing - 23rd International Conference, 2016

Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization.

Thomas Villmann, Marika Kaden, David Nebel, Andrea Bohnsack

Proceedings of the Artificial Intelligence and Soft Computing, 2016

Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities.

Marika Kaden, David Nebel, Thomas Villmann

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

2015

Median variants of learning vector quantization for learning of dissimilarity data.

David Nebel, Barbara Hammer, Kathleen Frohberg, Thomas Villmann

Neurocomputing, 2015

Building the Library of Rna 3D Nucleotide Conformations Using the Clustering Approach.

Tomasz Zok, Maciej Antczak, Martin Riedel, David Nebel, Thomas Villmann, Piotr Lukasiak, Jacek Blazewicz, Marta Szachniuk

Int. J. Appl. Math. Comput. Sci., 2015

Median-LVQ for classification of dissimilarity data based on ROC-optimization.

David Nebel, Thomas Villmann

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection.

Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl

Proceedings of the Computer Analysis of Images and Patterns, 2015

2014

Lateral enhancement in adaptive metric learning for functional data.

Thomas Villmann, Marika Kaden, David Nebel, Martin Riedel

Neurocomputing, 2014

Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization.

Mandy Lange, David Nebel, Thomas Villmann

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2014

Generative versus Discriminative Prototype Based Classification.

Barbara Hammer, David Nebel, Martin Riedel, Thomas Villmann

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2014

Rejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches.

Lydia Fischer, David Nebel, Thomas Villmann, Barbara Hammer, Heiko Wersing

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2014

Non-euclidean Principal Component Analysis for Matrices by Hebbian Learning.

Mandy Lange, David Nebel, Thomas Villmann

Proceedings of the Artificial Intelligence and Soft Computing, 2014

Supervised Generative Models for Learning Dissimilarity Data.

David Nebel, Barbara Hammer, Thomas Villmann

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

2013

A Median Variant of Generalized Learning Vector Quantization.

David Nebel, Barbara Hammer, Thomas Villmann

Proceedings of the Neural Information Processing - 20th International Conference, 2013

2012

ICMLA Face Recognition Challenge - Results of the Team Computational Intelligence Mittweida.

Thomas Villmann, Marika Kästner, David Nebel, Martin Riedel

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

Differentiable Kernels in Generalized Matrix Learning Vector Quantization.

Marika Kästner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012