| 2016 | ESANN | Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. | Marika Kaden, David Nebel, Thomas Villmann |
| 2016 | ICAISC | 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 |
| 2016 | ICONIP | Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. | Sascha Saralajew, David Nebel, Thomas Villmann |
| 2015 | CAIP | Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. | Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl |
| 2015 | ESANN | Median-LVQ for classification of dissimilarity data based on ROC-optimization. | David Nebel, Thomas Villmann |
| 2014 | ESANN | Supervised Generative Models for Learning Dissimilarity Data. | David Nebel, Barbara Hammer, Thomas Villmann |
| 2014 | ICAISC | Non-euclidean Principal Component Analysis for Matrices by Hebbian Learning. | Mandy Lange, David Nebel, Thomas Villmann |
| 2013 | ICONIP | A Median Variant of Generalized Learning Vector Quantization. | David Nebel, Barbara Hammer, Thomas Villmann |
| 2012 | ICMLA | Differentiable Kernels in Generalized Matrix Learning Vector Quantization. | Marika Kstner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann |
| 2012 | ICMLA | ICMLA Face Recognition Challenge - Results of the Team Computational Intelligence Mittweida. | Thomas Villmann, Marika Kstner, David Nebel, Martin Riedel |