Marika Kaden

60 papers B 12C 1Journal 20Unranked 24
YearRankTypeTitle / Venue / Authors
2026 J jnl
FA(IR)2MA-GLVQ - A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization.
Neurocomputing
Marika Kaden, Ronny Schubert, Julius Voigt, Lynn V. Reuss, Alexander Engelsberger, Sofie Lövdal, Elina L. van den Brandhof, Michael Biehl, Thomas Villmann
2025 — conf
Reliable Classification Learning for Medical Data Analysis Using Prototype-Based Models.
ICAISC (1)
Julius Voigt, Marika Kaden, Lynn V. Reuss, Thomas Villmann
2024 — ed.
Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond - Proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024
WSOM+
Thomas Villmann, Marika Kaden, Tina Geweniger, Frank-Michael Schleif
2024 — conf
Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge.
BIOSTEC (1)
Julius Voigt, Sascha Saralajew, Marika Kaden, Katrin Sophie Bohnsack, Lynn V. Reuss, Thomas Villmann
2024 B conf
Domain Knowledge Integration in Machine Learning Systems - An Introduction.
ESANN
Marika Kaden, Sascha Saralajew, Thomas Villmann
2024 — conf
Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation.
WSOM+
Marika Kaden, Julius Voigt, Katrin Sophie Bohnsack, Mandy Lange-Geisler, Thomas Villmann
2023 — conf
A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation.
WHISPERS
Ronny Schubert, Lynn V. Reuss, Daniel Staps, Marika Kaden, Thomas Villmann, Robert Hasler, Robin Herz, Till Tiemann, Wolfram Richardt
2023 J jnl
Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective.
IEEE ACM Trans. Comput. Biol. Bioinform.
Katrin Sophie Bohnsack, Marika Kaden, Julia Abel, Thomas Villmann
2023 — conf
Compression of Particle Images for Inspection of Microgravity Experiments by Means of a Symmetric Structural Auto-Encoder.
WHISPERS
Daniel Staps, Marika Kaden, Jan Auth, Florian Zaussinger, Thomas Villmann
2023 — conf
Efficient Representation of Biochemical Structures for Supervised and Unsupervised Machine Learning Models Using Multi-Sensoric Embeddings.
BIOINFORMATICS
Katrin Sophie Bohnsack, Alexander Engelsberger, Marika Kaden, Thomas Villmann
2023 J jnl
Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data.
Neurocomputing
Katrin Sophie Bohnsack, Julius Voigt, Marika Kaden, Florian Heinke, Thomas Villmann
2023 B conf
Variants of Neural Gas for Regression Learning.
ESANN
Thomas Villmann, Ronny Schubert, Marika Kaden
2022 B conf
A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation.
IDA
Thomas Villmann, Daniel Staps, Jensun Ravichandran, Sascha Saralajew, Michael Biehl, Marika Kaden
2022 B conf
Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features.
ESANN
Katrin Sophie Bohnsack, Marika Kaden, Julius Voigt, Thomas Villmann
2022 J jnl
Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.
Neural Comput. Appl.
Marika Kaden, Katrin Sophie Bohnsack, Mirko Weber, Mateusz Kudla, Kaja Gutowska, Jacek Blazewicz, Thomas Villmann
2022 B conf
Prototype-based One-Class-Classification Learning Using Local Representations.
IJCNN
Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann
2022 J jnl
Quantum-inspired learning vector quantizers for prototype-based classification.
Neural Comput. Appl.
Thomas Villmann, Alexander Engelsberger, Jensun Ravichandran, Andrea Villmann, Marika Kaden
2022 — conf
Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models.
ICONIP (2)
Danny Möbius, Jensun Ravichandran, Marika Kaden, Thomas Villmann
2022 J jnl
Variants of recurrent learning vector quantization.
Neurocomputing
Jensun Ravichandran, Marika Kaden, Thomas Villmann
2021 J jnl
AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task.
Sensors
Feryel Zoghlami, Marika Kaden, Thomas Villmann, Germar Schneider, Harald Heinrich
2021 — conf
Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks.
ICAISC (1)
Seyedfakhredin Musavishavazi, Marika Kaden, Thomas Villmann
2021 B conf
RecLVQ: Recurrent Learning Vector Quantization.
ESANN
Jensun Ravichandran, Thomas Villmann, Marika Kaden
2021 — conf
Sensors data fusion for smart decisions making: A novel bi-functional system for the evaluation of sensors contribution in classification problems.
ICIT
Feryel Zoghlami, Marika Kaden, Thomas Villmann, Germar Schneider, Harald Heinrich
2021 B conf
The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach.
ESANN
Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann
2021 J jnl
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers.
Entropy
Katrin Sophie Bohnsack, Marika Kaden, Julia Abel, Sascha Saralajew, Thomas Villmann
2020 J jnl
Learning vector quantization and relevances in complex coefficient space.
Neural Comput. Appl.
Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert
2020 B conf
Quantum-Inspired Learning Vector Quantization for Classification Learning.
ESANN
Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden
2020 J jnl
Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability.
Neurocomputing
Jensun Ravichandran, Marika Kaden, Sascha Saralajew, Thomas Villmann
2019 J jnl
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison.
CoRR
Thomas Villmann, John Ravichandran, Andrea Villmann, David Nebel, Marika Kaden
2019 J jnl
Application of an interpretable classification model on Early Folding Residues during protein folding.
BioData Min.
Sebastian Bittrich, Marika Kaden, Christoph Leberecht, Florian Kaiser, Thomas Villmann, Dirk Labudde
2019 — conf
Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis.
ICAISC (2)
Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann
2019 — conf
Investigation of Activation Functions for Generalized Learning Vector Quantization.
WSOM+
Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden
2019 — conf
Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization.
WSOM+
Thomas Villmann, Marika Kaden, Szymon Wasik, Mateusz Kudla, Kaja Gutowska, Andrea Villmann, Jacek Blazewicz
2018 — conf
Investigating the Influence of CPU Load, Memory Usage and Environmental Conditions on the Jittering of Android Devices.
ICNCC
Maik Benndorf, Marika Kaden, Frederic Ringsleben, Christian Roschke, Rico Thomanek, Martin Gaedke, Thomas Haenselmann
2018 J jnl
Learning vector quantization classifiers for ROC-optimization.
Comput. Stat.
Thomas Villmann, Marika Kaden, Wieland Hermann, Michael Biehl
2018 — conf
Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning.
ICAISC (1)
Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann
2018 B conf
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach.
ESANN
Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann
2017 J jnl
Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning.
J. Artif. Intell. Soft Comput. Res.
Thomas Villmann, Andrea Bohnsack, Marika Kaden
2017 — conf
Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities.
WSOM
Marika Kaden, David Nebel, Friedrich Melchert, Andreas Backhaus, Udo Seiffert, Thomas Villmann
2017 — conf
Prototypes and matrix relevance learning in complex fourier space.
WSOM
Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert
2017 J jnl
Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning.
Neurocomputing
David Nebel, Marika Kaden, Andrea Villmann, Thomas Villmann
2016 B conf
Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities.
ESANN
Marika Kaden, David Nebel, Thomas Villmann
2016 — conf
Complex Variants of GLVQ Based on Wirtinger's Calculus.
WSOM
Matthias Gay, Marika Kaden, Michael Biehl, Alexander Lampe, Thomas Villmann
2016 — —
Integration of Auxiliary Data Knowledge in Prototype Based Vector Quantization and Classification Models
Marika Kaden
2016 J jnl
Learning matrix quantization and relevance learning based on Schatten-p-norms.
Neurocomputing
Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
2016 — conf
Low-Rank Kernel Space Representations in Prototype Learning.
WSOM
Kerstin Bunte, Marika Kaden, Frank-Michael Schleif
2016 — conf
Self-Adjusting Reject Options in Prototype Based Classification.
WSOM
Thomas Villmann, Marika Kaden, Andrea Bohnsack, J.-M. Villmann, T. Drogies, Sascha Saralajew, Barbara Hammer
2016 — conf
Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization.
ICAISC (2)
Thomas Villmann, Marika Kaden, David Nebel, Andrea Bohnsack
2015 J jnl
Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines.
Soft Comput.
Marika Kaden, Martin Riedel, Wieland Hermann, Thomas Villmann
2015 J jnl
Kernelized vector quantization in gradient-descent learning.
Neurocomputing
Thomas Villmann, Sven Haase, Marika Kaden
2015 — conf
Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection.
CAIP (2)
Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl
2015 B conf
Learning matrix quantization and variants of relevance learning.
ESANN
Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
2015 — conf
Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms.
ICAISC (1)
Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann
2014 — ed.
Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014
WSOM
Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange
2014 — conf
Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment.
WSOM
Marika Kaden, Wieland Hermann, Thomas Villmann
2014 J jnl
Lateral enhancement in adaptive metric learning for functional data.
Neurocomputing
Thomas Villmann, Marika Kaden, David Nebel, Martin Riedel
2014 B conf
Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization.
ESANN
Marika Kaden, Wieland Hermann, Thomas Villmann
2014 C conf
Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems.
CIDM
Thomas Villmann, Marika Kaden, Mandy Lange, Paul Sturmer, Wieland Hermann
2014 — conf
RFSOM - Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation.
WSOM
Mathias Klingner, Sven Hellbach, Martin Riedel, Marika Kaden, Thomas Villmann, Hans-Joachim Böhme
2013 J jnl
Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters.
Comput. Intell. Neurosci.
Tina Geweniger, Lydia Fischer, Marika Kaden, Mandy Lange, Thomas Villmann