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Andrea Villmann
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2020 – today
- 2023
- [c9]Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann, Thomas Villmann
:
The Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers. ICAISC (1) 2023: 134-144 - 2022
- [j2]Thomas Villmann
Quantum-inspired learning vector quantizers for prototype-based classification. Neural Comput. Appl. 34(1): 79-88 (2022) - 2020
- [c8]Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden:
Quantum-Inspired Learning Vector Quantization for Classification Learning. ESANN 2020: 279-284
2010 – 2019
- 2019
- [c7]Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann:
Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. ICAISC (2) 2019: 443-454 - [c6]Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden:
Investigation of Activation Functions for Generalized Learning Vector Quantization. WSOM+ 2019: 179-188 - [c5]Thomas Villmann, Marika Kaden, Szymon Wasik, Mateusz Kudla, Kaja Gutowska
Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization. WSOM+ 2019: 324-333 - [i1]Thomas Villmann, John Ravichandran, Andrea Villmann, David Nebel, Marika Kaden:
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison. CoRR abs/1901.05995 (2019) - 2018
- [c4]Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann:
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. ESANN 2018 - [c3]Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann:
Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. ICAISC (1) 2018: 724-735 - 2017
- [j1]David Nebel, Marika Kaden, Andrea Villmann, Thomas Villmann:
Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning. Neurocomputing 268: 42-54 (2017) - [c2]Mohammad Mohammadi, Michael Biehl
Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. ICAISC (1) 2017: 131-142 - [c1]Thomas Villmann, Michael Biehl
Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. WSOM 2017: 69-76
Coauthor Index
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