Supervised learning in committee machines by PCA. - Christoph Bunzmann; Michael Biehl; Robert Urbanczik (ESANN, 2002)

The dynamics of Learning Vector Quantization. - Michael Biehl; Anarta Ghosh; Barbara Hammer (ESANN, 2005)

LVQ acrosome integrity assessment of boar sperm cells. - Nikolai Petkov; Enrique Alegre; Michael Biehl; Lidia Sánchez (CompIMAGE, 2006)

Classification of Boar Sperm Head Images using Learning Vector Quantization. - Michael Biehl; Piter Pasma; Marten Pijl; Lidia Sánchez; Nicolai Petkov (ESANN, 2006)

Learning vector quantization: The dynamics of winner-takes-all algorithms. - Michael Biehl; Anarta Ghosh; Barbara Hammer (Neurocomputing, 2006)

Performance analysis of LVQ algorithms: A statistical physics approach. - Anarta Ghosh; Michael Biehl; Barbara Hammer (Neural Networks, 2006)

07131 Summary – Similarity-based Clustering and its Application to Medicine and Biology. - Michael Biehl; Barbara Hammer; Michel Verleysen; Thomas Villmann (Similarity-based Clustering and its Application to Medicine and Biology, 2007)

07131 Abstracts Collection – Similarity-based Clustering and its Application to Medicine and Biology. - Michael Biehl; Barbara Hammer; Michel Verleysen; Thomas Villmann (Similarity-based Clustering and its Application to Medicine and Biology, 2007)

Learning Vector Quantization: generalization ability and dynamics of competing prototypes. - Aree Witoelar; Michael Biehl; Barbara Hammer (Similarity-based Clustering and its Application to Medicine and Biology, 2007)

Relevance matrices in LVQ. - Petra Schneider; Michael Biehl; Barbara Hammer (ESANN, 2007)

On the dynamics of Vector Quantization and Neural Gas. - Aree Witoelar; Michael Biehl; Anarta Ghosh; Barbara Hammer (ESANN, 2007)

Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning. - Michael Biehl; Rainer Breitling; Yang Li 036 (IDEAL, 2007)

Advances in computational intelligence and learning. - Michael Biehl; Erzsébet Merényi; Fabrice Rossi (Neurocomputing, 2007)

Dynamics and Generalization Ability of LVQ Algorithms. - Michael Biehl; Anarta Ghosh; Barbara Hammer (J. Mach. Learn. Res., 2007)

Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. - Marc Strickert; Petra Schneider; Jens Keilwagen; Thomas Villmann; Michael Biehl; Barbara Hammer (ANNPR, 2008)

Generalized matrix learning vector quantizer for the analysis of spectral data. - Petra Schneider; Frank-Michael Schleif; Thomas Villmann; Michael Biehl (ESANN, 2008)

Phase transitions in Vector Quantization. - Aree Witoelar; Anarta Ghosh; Michael Biehl (ESANN, 2008)

Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. - Enrique Alegre; Michael Biehl; Nicolai Petkov; Lidia Sánchez (Comput. Biol. Medicine, 2008)

Progress in modeling, theory, and application of computational intelligence. - Fabrice Rossi; Michael Biehl; Cecilio Angulo Bahón (Neurocomputing, 2008)

Learning dynamics and robustness of vector quantization and neural gas. - Aree Witoelar; Michael Biehl; Anarta Ghosh; Barbara Hammer (Neurocomputing, 2008)

Nonlinear Dimension Reduction and Visualization of Labeled Data. - Kerstin Bunte; Barbara Hammer; Michael Biehl (CAIP, 2009)

Statistical Mechanics of On-line Learning. - Michael Biehl; Nestor Caticha; Peter Riegler (Similarity-Based Clustering, 2009)

09081 Abstracts Collection - Similarity-based learning on structures. - Michael Biehl; Barbara Hammer; Sepp Hochreiter; Stefan C. Kremer; Thomas Villmann (Similarity-based learning on structures, 2009)

09081 Summary - Similarity-based learning on structures. - Michael Biehl; Barbara Hammer; Sepp Hochreiter; Stefan C. Kremer; Thomas Villmann (Similarity-based learning on structures, 2009)

Some Theoretical Aspects of the Neural Gas Vector Quantizer. - Thomas Villmann; Barbara Hammer; Michael Biehl (Similarity-Based Clustering, 2009)

Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition. - Gert-Jan de Vries; Michael Biehl (Similarity-based learning on structures, 2009)

Adaptive Metrics for Content Based Image Retrieval in Dermatology. - Kerstin Bunte; Michael Biehl; Nicolai Petkov; Marcel F. Jonkman (ESANN, 2009)

Nonlinear Discriminative Data Visualization. - Kerstin Bunte; Barbara Hammer; Petra Schneider; Michael Biehl (ESANN, 2009)

Hyperparameter Learning in Robust Soft LVQ. - Petra Schneider; Michael Biehl; Barbara Hammer (ESANN, 2009)

Equilibrium properties of off-line LVQ. - Aree Witoelar; Michael Biehl; Barbara Hammer (ESANN, 2009)

Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. - Marc Strickert; Jens Keilwagen; Frank-Michael Schleif; Thomas Villmann; Michael Biehl (IWANN (1), 2009)

Advances in machine learning and computational intelligence. - Frank-Michael Schleif; Michael Biehl; Alfredo Vellido (Neurocomputing, 2009)

Phase transitions in vector quantization and neural gas. - Aree Witoelar; Michael Biehl (Neurocomputing, 2009)

Distance Learning in Discriminative Vector Quantization. - Petra Schneider; Michael Biehl; Barbara Hammer (Neural Comput., 2009)

Adaptive Relevance Matrices in Learning Vector Quantization. - Petra Schneider; Michael Biehl; Barbara Hammer (Neural Comput., 2009)

Metric Learning for Prototype-Based Classification. - Michael Biehl; Barbara Hammer; Petra Schneider; Thomas Villmann (Innovations in Neural Information Paradigms and Applications, 2009)

Early Nervous Systems - Theoretical Background and a Preliminary Model of Neuronal Processes. - Ot de Wiljes; Ronald A. J. van Elburg; Michael Biehl; Fred Keijzer (ALIFE, 2010)

The Mathematics of Divergence Based Online Learning in Vector Quantization. - Thomas Villmann; Sven Haase; Frank-Michael Schleif; Barbara Hammer; Michael Biehl (ANNPR, 2010)

Some steps towards a general principle for dimensionality reduction mappings. - Barbara Hammer; Kerstin Bunte; Michael Biehl (Learning paradigms in dynamic environments, 2010)

Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. - Kerstin Bunte; Barbara Hammer; Thomas Villmann; Michael Biehl; Axel Wismüller (ESANN, 2010)

Divergence based Learning Vector Quantization. - Ernest Mwebaze; Petra Schneider; Frank-Michael Schleif; Sven Haase; Thomas Villmann; Michael Biehl (ESANN, 2010)

Generalized Derivative Based Kernelized Learning Vector Quantization. - Frank-Michael Schleif; Thomas Villmann; Barbara Hammer; Petra Schneider; Michael Biehl (IDEAL, 2010)

Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data. - Kerstin Bunte; Barbara Hammer; Axel Wismüller; Michael Biehl (Neurocomputing, 2010)

Hyperparameter learning in probabilistic prototype-based models. - Petra Schneider; Michael Biehl; Barbara Hammer (Neurocomputing, 2010)

Window-Based Example Selection in Learning Vector Quantization. - Aree Witoelar; Anarta Ghosh; J. J. G. de Vries; Barbara Hammer; Michael Biehl (Neural Comput., 2010)

Regularization in matrix relevance learning. - Petra Schneider; Kerstin Bunte; Han Stiekema; Barbara Hammer; Thomas Villmann; Michael Biehl (IEEE Trans. Neural Networks, 2010)

Adaptive Matrices for Color Texture Classification. - Kerstin Bunte; Ioannis Giotis 002; Nicolai Petkov; Michael Biehl (CAIP (2), 2011)

Dimensionality reduction mappings. - Kerstin Bunte; Michael Biehl; Barbara Hammer (CIDM, 2011)

Supervised dimension reduction mappings. - Kerstin Bunte; Michael Biehl; Barbara Hammer (ESANN, 2011)

Generalized functional relevance learning vector quantization. - Marika Kästner; Barbara Hammer; Michael Biehl; Thomas Villmann (ESANN, 2011)

Causal relevance learning for robust classification under interventions. - Ernest Mwebaze; John A. Quinn; Michael Biehl (ESANN, 2011)

Learning of causal relations. - John A. Quinn; Joris M. Mooij; Tom Heskes; Michael Biehl (ESANN, 2011)

Multivariate class labeling in Robust Soft LVQ. - Petra Schneider; Tina Geweniger; Frank-Michael Schleif; Michael Biehl; Thomas Villmann (ESANN, 2011)

Texture feature selection with relevance learning to classify interstitial lung disease patterns. - Markus B. Huber; Kerstin Bunte; Mahesh B. Nagarajan; Michael Biehl; Lawrence A. Ray; Axel Wismüller (Computer-Aided Diagnosis, 2011)

A General Framework for Dimensionality Reduction for Large Data Sets. - Barbara Hammer; Michael Biehl; Kerstin Bunte; Bassam Mokbel (WSOM, 2011)

How to Evaluate Dimensionality Reduction? - Improving the Co-ranking Matrix - Wouter Lueks; Bassam Mokbel; Michael Biehl; Barbara Hammer (CoRR, 2011)

Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). - Michael Biehl; Barbara Hammer; Erzsébet Merényi; Alessandro Sperduti; Thomas Villmann (Dagstuhl Reports, 2011)

Neighbor embedding XOM for dimension reduction and visualization. - Kerstin Bunte; Barbara Hammer; Thomas Villmann; Michael Biehl; Axel Wismüller (Neurocomputing, 2011)

Divergence-based classification in learning vector quantization. - Ernest Mwebaze; Petra Schneider; Frank-Michael Schleif; Jennifer R. Aduwo; John A. Quinn; Sven Haase; Thomas Villmann; Michael Biehl (Neurocomputing, 2011)

Learning effective color features for content based image retrieval in dermatology. - Kerstin Bunte; Michael Biehl; Marcel F. Jonkman; Nicolai Petkov (Pattern Recognit., 2011)

Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. - Michael Biehl; Petra Schneider; David Smith; Han Stiekema; Angela Taylor; Beverly Hughes; Cedric Shackleton; Paul Stewart; Wiebke Arlt (ESANN, 2012)

Adaptive learning for complex-valued data. - Kerstin Bunte; Frank-Michael Schleif; Michael Biehl (ESANN, 2012)

Visualizing the quality of dimensionality reduction. - Bassam Mokbel; Wouter Lueks; Andrej Gisbrecht; Michael Biehl; Barbara Hammer (ESANN, 2012)

Differentiable Kernels in Generalized Matrix Learning Vector Quantization. - Marika Kästner; David Nebel; Martin Riedel; Michael Biehl; Thomas Villmann (ICMLA (1), 2012)

Large margin linear discriminative visualization by Matrix Relevance Learning. - Michael Biehl; Kerstin Bunte; Frank-Michael Schleif; Petra Schneider; Thomas Villmann (IJCNN, 2012)

Visualization of processes in self-learning systems. - Gabriele Peters; Kerstin Bunte; Marc Strickert; Michael Biehl; Thomas Villmann (PST, 2012)

Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects. - Michael Biehl; Marika Kästner; Mandy Lange; Thomas Villmann (WSOM, 2012)

Texture feature ranking with relevance learning to classify interstitial lung disease patterns. - Markus B. Huber; Kerstin Bunte; Mahesh B. Nagarajan; Michael Biehl; Lawrence A. Ray; Axel Wismüller (Artif. Intell. Medicine, 2012)

Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. - Kerstin Bunte; Sven Haase; Michael Biehl; Thomas Villmann (Neurocomputing, 2012)

Functional relevance learning in generalized learning vector quantization. - Marika Kästner; Barbara Hammer; Michael Biehl; Thomas Villmann (Neurocomputing, 2012)

Admire LVQ - Adaptive Distance Measures in Relevance Learning Vector Quantization. - Michael Biehl (Künstliche Intell., 2012)

A General Framework for Dimensionality-Reducing Data Visualization Mapping. - Kerstin Bunte; Michael Biehl; Barbara Hammer (Neural Comput., 2012)

Limited Rank Matrix Learning, discriminative dimension reduction and visualization. - Kerstin Bunte; Petra Schneider; Barbara Hammer; Frank-Michael Schleif; Thomas Villmann; Michael Biehl (Neural Networks, 2012)

Distance Measures for Prototype Based Classification. - Michael Biehl; Barbara Hammer; Thomas Villmann (BrainComp, 2013)

Regularization and improved interpretation of linear data mappings and adaptive distance measures. - Marc Strickert; Barbara Hammer; Thomas Villmann; Michael Biehl (CIDM, 2013)

Non-Euclidean independent component analysis and Oja's learning. - Mandy Lange; Michael Biehl; Thomas Villmann (ESANN, 2013)

Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ. - Enrique Alegre; Michael Biehl; Nicolai Petkov; Lidia Sánchez (Comput. Methods Programs Biomed., 2013)

Adaptive Matrices and Filters for Color Texture Classification. - Ioannis Giotis 002; Kerstin Bunte; Nicolai Petkov; Michael Biehl (J. Math. Imaging Vis., 2013)

Valid interpretation of feature relevance for linear data mappings. - Benoît Frénay; Daniela Hofmann; Alexander Schulz 001; Michael Biehl; Barbara Hammer (CIDM, 2014)

Segmented shape-symbolic time series representation. - Herbert Teun Kruitbosch; Ioannis Giotis 002; Michael Biehl (ESANN, 2014)

Prototype-Based Classifiers and Their Application in the Life Sciences. - Michael Biehl (WSOM, 2014)

Erratum to: Adaptive Matrices and Filters for Color Texture Classification. - Ioannis Giotis 002; Kerstin Bunte; Nicolai Petkov; Michael Biehl (J. Math. Imaging Vis., 2014)

Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ. - Michael Biehl; Deborah Mudali; Klaus Leonard Leenders; Jos B. T. M. Roerdink (BrainComp, 2015)

Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. - Thomas Villmann; Marika Kaden; David Nebel; Michael Biehl (CAIP (2), 2015)

Facial Expression Recognition Using Learning Vector Quantization. - Gert-Jan de Vries; Steffen Pauws; Michael Biehl (CAIP (2), 2015)

Combining dissimilarity measures for prototype-based classification. - Ernest Mwebaze; Gjalt Bearda; Michael Biehl; Dietlind Zühlke (ESANN, 2015)

Stationarity of Matrix Relevance LVQ. - Michael Biehl; Barbara Hammer; Frank-Michael Schleif; Petra Schneider; Thomas Villmann (IJCNN, 2015)

Inferring Feature Relevances From Metric Learning. - Alexander Schulz 001; Bassam Mokbel; Michael Biehl; Barbara Hammer (SSCI, 2015)

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge. - Michael Biehl; Peter J. Sadowski; Gyan Bhanot; Erhan Bilal; Adel Dayarian; Pablo Meyer 001; Raquel Norel; Kahn Rhrissorrakrai; Michael D. Zeller; Sahand Hormoz (Bioinform., 2015)

Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge. - Adel Dayarian; Roberto Romero; Zhiming Wang; Michael Biehl; Erhan Bilal; Sahand Hormoz; Pablo Meyer 001; Raquel Norel; Kahn Rhrissorrakrai; Gyan Bhanot; Feng Luo 001; Adi L. Tarca (Bioinform., 2015)

Inter-species inference of gene set enrichment in lung epithelial cells from proteomic and large transcriptomic datasets. - Sahand Hormoz; Gyan Bhanot; Michael Biehl; Erhan Bilal; Pablo Meyer 001; Raquel Norel; Kahn Rhrissorrakrai; Adel Dayarian (Bioinform., 2015)

MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images. - Ioannis Giotis 002; Nynke Molders; Sander Land; Michael Biehl; Marcel F. Jonkman; Nicolai Petkov (Expert Syst. Appl., 2015)

Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution. - Oltman Ottes de Wiljes; Ronald A. J. van Elburg; Michael Biehl; Fred Keijzer (Frontiers Comput. Neurosci., 2015)

Developments in computational intelligence and machine learning. - Michael Biehl; Alessandro Ghio; Frank-Michael Schleif (Neurocomputing, 2015)

Non-Euclidean principal component analysis by Hebbian learning. - Mandy Lange; Michael Biehl; Thomas Villmann (Neurocomputing, 2015)

Insightful stress detection from physiology modalities using Learning Vector Quantization. - Gert-Jan de Vries; Steffen C. Pauws; Michael Biehl (Neurocomputing, 2015)

Towards Emotion Classification Using Appraisal Modeling. - Gert-Jan de Vries; Paul Lemmens; Dirk Brokken; Steffen Pauws; Michael Biehl (Int. J. Synth. Emot., 2015)

Prototype-based Models for the Supervised Learning of Classification Schemes. - Michael Biehl; Barbara Hammer; Thomas Villmann (Astroinformatics, 2016)

Predicting recurrence in clear cell Renal Cell Carcinoma: Analysis of TCGA data using outlier analysis and generalized matrix LVQ. - Gargi Mukherjee; Gyan Bhanot; Kevin Raines; Srikanth Sastry; Sebastian Doniach; Michael Biehl (CEC, 2016)

Complex Variants of GLVQ Based on Wirtinger's Calculus. - Matthias Gay; Marika Kaden; Michael Biehl; Alexander Lampe; Thomas Villmann (WSOM, 2016)

Functional Representation of Prototypes in LVQ and Relevance Learning. - Friedrich Melchert; Udo Seiffert; Michael Biehl (WSOM, 2016)

LVQ and SVM Classification of FDG-PET Brain Data. - Deborah Mudali; Michael Biehl; Klaus Leonard Leenders; Jos B. T. M. Roerdink (WSOM, 2016)

Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis. - Ernest Mwebaze; Michael Biehl (WSOM, 2016)

Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261). - Gyan Bhanot; Michael Biehl; Thomas Villmann; Dietlind Zühlke (Dagstuhl Reports, 2016)

Odor recognition in robotics applications by discriminative time-series modeling. - Frank-Michael Schleif; Barbara Hammer; Javier Gonzalez Monroy; Javier González Jiménez 001; José Luis Blanco-Claraco; Michael Biehl; Nicolai Petkov (Pattern Anal. Appl., 2016)

Biomedical Applications of Prototype Based Classifiers and Relevance Learning. - Michael Biehl (AlCoB, 2017)

Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. - Gyan Bhanot; Michael Biehl; Thomas Villmann; Dietlind Zühlke (ESANN, 2017)

Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders. - Sreejita Ghosh; Elizabeth Sarah Baranowski; Rick van Veen; Gert-Jan de Vries; Michael Biehl; Wiebke Arlt; Peter Tiño; Kerstin Bunte (ESANN, 2017)

Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. - Mohammad Mohammadi; Michael Biehl; Andrea Villmann; Thomas Villmann (ICAISC (1), 2017)

Marker selection for the detection of trisomy 21 using generalized matrix learning vector quantization. - Andreas C. Neocleous; Costas Neocleous; Christos N. Schizas; Michael Biehl; Nicolai Petkov (IJCNN, 2017)

Empirical evaluation of gradient methods for matrix learning vector quantization. - Michael LeKander; Michael Biehl; Harm de Vries (WSOM, 2017)

Prototypes and matrix relevance learning in complex fourier space. - Michiel Straat; Marika Kaden; Matthias Gay; Thomas Villmann; Alexander Lampe; Udo Seiffert; Michael Biehl; Friedrich Melchert (WSOM, 2017)

Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. - Thomas Villmann; Michael Biehl; Andrea Villmann; Sascha Saralajew (WSOM, 2017)

Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases. - Rick van Veen; Lidia Talavera-Martínez; Rosalie V. Kogan; Sanne K. Meles; Deborah Mudali; Jos B. T. M. Roerdink; Federico Massa; M. Grazzini; Jose A. Obeso; Maria C. Rodriguez-Oroz; Klaus Leonard Leenders; Remco J. Renken; et al. (APPIS, 2018)

Machine learning and data analysis in astroinformatics. - Michael Biehl; Kerstin Bunte; Giuseppe Longo; Peter Tiño (ESANN, 2018)

Prototype-based analysis of GAMA galaxy catalogue data. - Aleke Nolte; Lingyu Wang; Michael Biehl (ESANN, 2018)

Learning vector quantization classifiers for ROC-optimization. - Thomas Villmann; Marika Kaden; Wieland Hermann; Michael Biehl (Comput. Stat., 2018)

Statistical Mechanics of On-Line Learning Under Concept Drift. - Michiel Straat; Fthi Abadi; Christina Göpfert; Barbara Hammer; Michael Biehl (Entropy, 2018)

Advances in artificial neural networks, machine learning and computational intelligence. - Fabio Aiolli; Michael Biehl; Luca Oneto (Neurocomputing, 2018)

The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios. - Michael Biehl (BrainComp, 2019)

A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle. - Amey Bhole; Owen Falzon; Michael Biehl; George Azzopardi (CAIP (2), 2019)

Statistical physics of learning and inference. - Michael Biehl; Nestor Caticha; Manfred Opper; Thomas Villmann (ESANN, 2019)

Feature relevance bounds for ordinal regression. - Lukas Pfannschmidt; Jonathan Jakob; Michael Biehl; Peter Tiño; Barbara Hammer (ESANN, 2019)

On-line learning dynamics of ReLU neural networks using statistical physics techniques. - Michiel Straat; Michael Biehl (ESANN, 2019)

Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. - Michael Biehl; Fthi Abadi; Christina Göpfert; Barbara Hammer (WSOM+, 2019)

Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation. - Elisa Oostwal; Michiel Straat; Michael Biehl (CoRR, 2019)

Galaxy classification: A machine learning analysis of GAMA catalogue data. - Aleke Nolte; Lingyu Wang; Maciej Bilicki; Benne Holwerda; Michael Biehl (Neurocomputing, 2019)

Early detection of plant diseases using spectral data. - Godliver Owomugisha; Ephraim Nuwamanya; John A. Quinn; Michael Biehl; Ernest Mwebaze (APPIS, 2020)

A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field. - Godliver Owomugisha; Pius K. B. Mugagga; Friedrich Melchert; Ernest Mwebaze; John A. Quinn; Michael Biehl (COMPASS, 2020)

Structure Preserving Encoding of Non-euclidean Similarity Data. - Maximilian Münch; Christoph Raab; Michael Biehl; Frank-Michael Schleif (ICPRAM, 2020)

Complex-Valued Embeddings of Generic Proximity Data. - Maximilian Münch; Michiel Straat; Michael Biehl; Frank-Michael Schleif (S+SSPR, 2020)

An application of generalized matrix learning vector quantization in neuroimaging. - Rick van Veen; Vita Gurvits; Rosalie V. Kogan; Sanne K. Meles; Gert-Jan de Vries; Remco J. Renken; Maria C. Rodriguez-Oroz; Rafael Rodriguez-Rojas; Dario Arnaldi; Stefano Raffa; Bauke M. de Jong; Klaus Leonard Leenders; et al. (Comput. Methods Programs Biomed., 2020)

Data-Driven Supervised Learning for Life Science Data. - Maximilian Münch; Christoph Raab; Michael Biehl; Frank-Michael Schleif (Frontiers Appl. Math. Stat., 2020)

Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information. - Lukas Pfannschmidt; Jonathan Jakob; Fabian Hinder; Michael Biehl; Peter Tiño; Barbara Hammer (Neurocomputing, 2020)

Adaptive basis functions for prototype-based classification of functional data. - Friedrich Melchert; Gabriele Bani; Udo Seiffert; Michael Biehl (Neural Comput. Appl., 2020)

Learning vector quantization and relevances in complex coefficient space. - Michiel Straat; Marika Kaden; Matthias Gay; Thomas Villmann; Alexander Lampe; Udo Seiffert; Michael Biehl; Friedrich Melchert (Neural Comput. Appl., 2020)

Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases. - Godliver Owomugisha; Friedrich Melchert; Ernest Mwebaze; John A. Quinn; Michael Biehl (IEEE Access, 2021)

DECORAS: detection and characterization of radio-astronomical sources using deep learning. - Samira Rezaei; John P. McKean; Michael Biehl; Amir Javadpour 001 (CoRR, 2021)

sklvq: Scikit Learning Vector Quantization. - Rick van Veen; Michael Biehl; Gert-Jan de Vries (J. Mach. Learn. Res., 2021)

A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. - Thomas Villmann; Daniel Staps; Jensun Ravichandran; Sascha Saralajew; Michael Biehl; Marika Kaden (IDA, 2022)

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder. - Rick van Veen; Sanne K. Meles; Remco J. Renken; Fransje E. Reesink; Wolfgang H. Oertel; Annette Janzen; Gert-Jan de Vries; Klaus Leonard Leenders; Michael Biehl (Comput. Methods Programs Biomed., 2022)

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets. - Sreejita Ghosh; Elizabeth Sarah Baranowski; Michael Biehl; Wiebke Arlt; Peter Tino; Kerstin Bunte (CoRR, 2022)

A machine learning based approach to gravitational lens identification with the International LOFAR Telescope. - Samira Rezaei; John P. McKean; Michael Biehl; W. de Roo; A. Lafontaine (CoRR, 2022)

Supervised learning in the presence of concept drift: a modelling framework. - Michiel Straat; Fthi Abadi; Zhuoyun Kan; Christina Göpfert; Barbara Hammer; Michael Biehl (Neural Comput. Appl., 2022)

Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA). - Michael Biehl; Sofie Lövdal (ESANN, 2023)

Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis. - Frederieke Richert; Michiel Straat; Elisa Oostwal; Michael Biehl (ESANN, 2023)

On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions. - Frederieke Richert; Otavio Citton; Michael Biehl (ESANN, 2024)

Interpreting Hybrid AI through Autodecoded Latent Space Entities. - Roland Veen; Christodoulos Hadjichristodoulou; Michael Biehl (ESANN, 2024)

A Graph-Theory Based fMRI Analysis. - Luca Barillaro; Marianna Milano; Maria Eugenia Caligiuri; Jelle R. Dalenberg; Giuseppe Agapito; Michael Biehl; Mario Cannataro (ICCS (6), 2024)

Interpretable Machine Learning in Endocrinology: A Diagnostic Tool in Primary Aldosteronism. - Michael Biehl; David Pavlov; Alice J. Sitch; Alessandro Prete; Wiebke Arlt (WSOM+, 2024)

Subspace corrected relevance learning with application in neuroimaging. - Rick van Veen; Neha Rajendra Bari Tamboli; Sofie Lövdal; Sanne K. Meles; Remco J. Renken; Gert-Jan de Vries; Dario Arnaldi; Silvia Morbelli; Pedro Clavero; José A. Obeso; Maria C. Rodriguez-Oroz; Klaus Leonard Leenders; et al. (Artif. Intell. Medicine, 2024)

Aligning Generalisation Between Humans and Machines. - Filip Ilievski; Barbara Hammer; Frank van Harmelen; Benjamin Paassen; Sascha Saralajew; Ute Schmid; Michael Biehl; Marianna Bolognesi; Xin Luna Dong; Kiril Gashteovski; Pascal Hitzler; Giuseppe Marra; et al. (CoRR, 2024)

Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces. - Sofie Lövdal; Michael Biehl (Neurocomputing, 2024)

Explainable machine learning for movement disorders - Classification of tremor and myoclonus. - Elina L. van den Brandhof; Inge Tuitert; A. M. Madelein van der Stouwe; Jan W. J. Elting; Jelle R. Dalenberg; Tatiana Svorenova; Marrit R. Klamer; Ramesh S. Marapin; Michael Biehl; Marina A. J. Tijssen (Comput. Biol. Medicine, 2025)

Hermite polynomials facilitating on-line learning analysis of layered neural networks with arbitrary activation function. - Otavio Citton; Frederieke Richert; Michael Biehl; Michiel Straat (Neurocomputing, 2025)

Interpretable modelling and visualization of biomedical data. - Sreejita Ghosh; Elizabeth Sarah Baranowski; Michael Biehl; Wiebke Arlt; Peter Tiño; Kerstin Bunte (Neurocomputing, 2025)

Discriminative Subspace Emersion from learning feature relevances across different populations. - Marco Canducci; Lida Abdi; Alessandro Prete; Roland J. Veen; Michael Biehl; Wiebke Arlt; Peter Tino (CoRR, 2025)