Michael Biehl

ORCID

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 61

Top Venues

Publications

Luca Barillaro, Marianna Milano, Maria Eugenia Caligiuri, Jelle R. Dalenberg, Giuseppe Agapito, Michael Biehl, Mario Cannataro
A Graph-Theory Based fMRI Analysis. ICCS (6) (2024)
Sofie Lövdal, Michael Biehl
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces. Neurocomputing 577 (2024)
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, Thomas Villmann, Michael Biehl
Subspace corrected relevance learning with application in neuroimaging. Artif. Intell. Medicine 149 (2024)
Sofie Lövdal, Michael Biehl
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces. CoRR (2024)
Michael Biehl, Sofie Lövdal
Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA). ESANN (2023)
Frederieke Richert, Michiel Straat, Elisa Oostwal, Michael Biehl
Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis. ESANN (2023)
Thomas Villmann, Daniel Staps, Jensun Ravichandran, Sascha Saralajew, Michael Biehl, Marika Kaden
A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. IDA (2022)
Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl
Supervised learning in the presence of concept drift: a modelling framework. Neural Comput. Appl. 34 (1) (2022)
Sreejita Ghosh, Elizabeth Sarah Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets. CoRR (2022)
S. Rezaei, John P. McKean, Michael Biehl, W. de Roo, A. Lafontaine
A machine learning based approach to gravitational lens identification with the International LOFAR Telescope. CoRR (2022)
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
FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder. Comput. Methods Programs Biomed. 225 (2022)
Godliver Owomugisha, Friedrich Melchert, Ernest Mwebaze, John A. Quinn, Michael Biehl
Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases. IEEE Access 9 (2021)
Rick van Veen, Michael Biehl, Gert-Jan de Vries
sklvq: Scikit Learning Vector Quantization. J. Mach. Learn. Res. 22 (2021)
Samira Rezaei, John P. McKean, Michael Biehl, Amir Javadpour
DECORAS: detection and characterization of radio-astronomical sources using deep learning. CoRR (2021)
Godliver Owomugisha, Ephraim Nuwamanya, John A. Quinn, Michael Biehl, Ernest Mwebaze
Early detection of plant diseases using spectral data. APPIS (2020)
Godliver Owomugisha, Pius K. B. Mugagga, Friedrich Melchert, Ernest Mwebaze, John A. Quinn, Michael Biehl
A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field. COMPASS (2020)
Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert
Learning vector quantization and relevances in complex coefficient space. Neural Comput. Appl. 32 (24) (2020)
Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl
Supervised Learning in the Presence of Concept Drift: A modelling framework. CoRR (2020)
Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
Complex-valued embeddings of generic proximity data. CoRR (2020)
Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif
Data-Driven Supervised Learning for Life Science Data. Frontiers Appl. Math. Stat. 6 (2020)
Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tiño, Barbara Hammer
Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information. Neurocomputing 416 (2020)
Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif
Structure Preserving Encoding of Non-euclidean Similarity Data. ICPRAM (2020)
Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
Complex-Valued Embeddings of Generic Proximity Data. S+SSPR (2020)
Friedrich Melchert, Gabriele Bani, Udo Seiffert, Michael Biehl
Adaptive basis functions for prototype-based classification of functional data. Neural Comput. Appl. 32 (24) (2020)
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, Michael Biehl
An application of generalized matrix learning vector quantization in neuroimaging. Comput. Methods Programs Biomed. 197 (2020)
Michael Biehl
The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios. BrainComp (2019)
Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tiño, Barbara Hammer
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. CoRR (2019)
Aleke Nolte, Lingyu Wang, Maciej Bilicki, Benne Holwerda, Michael Biehl
Galaxy classification: A machine learning analysis of GAMA catalogue data. Neurocomputing 342 (2019)
Elisa Oostwal, Michiel Straat, Michael Biehl
Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation. CoRR (2019)
Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer
Prototype-based classifiers in the presence of concept drift: A modelling framework. CoRR (2019)
Michiel Straat, Michael Biehl
On-line learning dynamics of ReLU neural networks using statistical physics techniques. ESANN (2019)
Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann
Statistical physics of learning and inference. ESANN (2019)
Amey Bhole, Owen Falzon, Michael Biehl, George Azzopardi
A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle. CAIP (2) (2019)
Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer
Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. WSOM+ (2019)
Aleke Nolte, Lingyu Wang, Maciej Bilicki, Benne Holwerda, Michael Biehl
Galaxy classification: A machine learning analysis of GAMA catalogue data. CoRR (2019)
Michiel Straat, Michael Biehl
On-line learning dynamics of ReLU neural networks using statistical physics techniques. CoRR (2019)
Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer
Feature relevance bounds for ordinal regression. ESANN (2019)
Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer
Feature Relevance Bounds for Ordinal Regression. CoRR (2019)
Michiel Straat, Fthi Abadi, Christina Göpfert, Barbara Hammer, Michael Biehl
Statistical Mechanics of On-Line Learning Under Concept Drift. Entropy 20 (10) (2018)
Fabio Aiolli, Michael Biehl, Luca Oneto
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 298 (2018)
Thomas Villmann, Marika Kaden, Wieland Hermann, Michael Biehl
Learning vector quantization classifiers for ROC-optimization. Comput. Stat. 33 (3) (2018)
Aleke Nolte, Lingyu Wang, Michael Biehl
Prototype-based analysis of GAMA galaxy catalogue data. ESANN (2018)
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, J. J. G. de Vries, Michael Biehl
Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases. APPIS (2018)
Michael Biehl, Kerstin Bunte, Giuseppe Longo, Peter Tiño
Machine learning and data analysis in astroinformatics. ESANN (2018)
Gabriele Bani, Udo Seiffert, Michael Biehl, Friedrich Melchert
Adaptive basis functions for prototype-based classification of functional data. WSOM (2017)
Michael Biehl
Biomedical Applications of Prototype Based Classifiers and Relevance Learning. AlCoB (2017)
Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke
Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. ESANN (2017)
Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert
Prototypes and matrix relevance learning in complex fourier space. WSOM (2017)
Michael LeKander, Michael Biehl, Harm de Vries
Empirical evaluation of gradient methods for matrix learning vector quantization. WSOM (2017)
Andreas C. Neocleous, Costas Neocleous, Christos N. Schizas, Michael Biehl, Nicolai Petkov
Marker selection for the detection of trisomy 21 using generalized matrix learning vector quantization. IJCNN (2017)
Sreejita Ghosh, Elizabeth Sarah Baranowski, Rick van Veen, Gert-Jan de Vries, Michael Biehl, Wiebke Arlt, Peter Tiño, Kerstin Bunte
Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders. ESANN (2017)
Thomas Villmann, Michael Biehl, Andrea Villmann, Sascha Saralajew
Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. WSOM (2017)
Mohammad Mohammadi, Michael Biehl, Andrea Villmann, Thomas Villmann
Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. ICAISC (1) (2017)
Frank-Michael Schleif, Barbara Hammer, Javier Gonzalez Monroy, Javier González Jiménez, José-Luis Blanco-Claraco, Michael Biehl, Nicolai Petkov
Odor recognition in robotics applications by discriminative time-series modeling. Pattern Anal. Appl. 19 (1) (2016)
Deborah Mudali, Michael Biehl, Klaus Leonard Leenders, Jos B. T. M. Roerdink
LVQ and SVM Classification of FDG-PET Brain Data. WSOM (2016)
Michael Biehl, Barbara Hammer, Thomas Villmann
Prototype-based Models for the Supervised Learning of Classification Schemes. Astroinformatics (2016)
Gargi Mukherjee, Gyan Bhanot, Kevin Raines, Srikanth Sastry, Sebastian Doniach, Michael Biehl
Predicting recurrence in clear cell Renal Cell Carcinoma: Analysis of TCGA data using outlier analysis and generalized matrix LVQ. CEC (2016)
Matthias Gay, Marika Kaden, Michael Biehl, Alexander Lampe, Thomas Villmann
Complex Variants of GLVQ Based on Wirtinger's Calculus. WSOM (2016)
Friedrich Melchert, Udo Seiffert, Michael Biehl
Functional Representation of Prototypes in LVQ and Relevance Learning. WSOM (2016)
Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke
Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261). Dagstuhl Reports 6 (6) (2016)
Ernest Mwebaze, Michael Biehl
Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis. WSOM (2016)
Mandy Lange, Michael Biehl, Thomas Villmann
Non-Euclidean principal component analysis by Hebbian learning. Neurocomputing 147 (2015)
Adel Dayarian, Roberto Romero, Zhiming Wang, Michael Biehl, Erhan Bilal, Sahand Hormoz, Pablo Meyer, Raquel Norel, Kahn Rhrissorrakrai, Gyan Bhanot, Feng Luo, Adi L. Tarca
Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge. Bioinform. 31 (4) (2015)
Michael Biehl, Peter J. Sadowski, Gyan Bhanot, Erhan Bilal, Adel Dayarian, Pablo Meyer, Raquel Norel, Kahn Rhrissorrakrai, Michael D. Zeller, Sahand Hormoz
Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge. Bioinform. 31 (4) (2015)
Michael Biehl, Deborah Mudali, Klaus Leonard Leenders, Jos B. T. M. Roerdink
Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ. BrainComp (2015)
Ernest Mwebaze, Gjalt Bearda, Michael Biehl, Dietlind Zühlke
Combining dissimilarity measures for prototype-based classification. ESANN (2015)
Gert-Jan de Vries, Steffen C. Pauws, Michael Biehl
Insightful stress detection from physiology modalities using Learning Vector Quantization. Neurocomputing 151 (2015)
Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
Stationarity of Matrix Relevance LVQ. IJCNN (2015)
Sahand Hormoz, Gyan Bhanot, Michael Biehl, Erhan Bilal, Pablo Meyer, Raquel Norel, Kahn Rhrissorrakrai, Adel Dayarian
Inter-species inference of gene set enrichment in lung epithelial cells from proteomic and large transcriptomic datasets. Bioinform. 31 (4) (2015)
Gert-Jan de Vries, Steffen Pauws, Michael Biehl
Facial Expression Recognition Using Learning Vector Quantization. CAIP (2) (2015)
Gert-Jan de Vries, Paul Lemmens, Dirk Brokken, Steffen Pauws, Michael Biehl
Towards Emotion Classification Using Appraisal Modeling. Int. J. Synth. Emot. 6 (1) (2015)
Michael Biehl, Alessandro Ghio, Frank-Michael Schleif
Developments in computational intelligence and machine learning. Neurocomputing 169 (2015)
Alexander Schulz, Bassam Mokbel, Michael Biehl, Barbara Hammer
Inferring Feature Relevances From Metric Learning. SSCI (2015)
Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl
Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. CAIP (2) (2015)
Ioannis Giotis, Nynke Molders, Sander Land, Michael Biehl, Marcel F. Jonkman, Nicolai Petkov
MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. 42 (19) (2015)
Oltman Ottes de Wiljes, Ronald A. J. van Elburg, Michael Biehl, Fred Keijzer
Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution. Frontiers Comput. Neurosci. 9 (2015)
Michael Biehl
Prototype-Based Classifiers and Their Application in the Life Sciences. WSOM (2014)
Ioannis Giotis, Kerstin Bunte, Nicolai Petkov, Michael Biehl
Erratum to: Adaptive Matrices and Filters for Color Texture Classification. J. Math. Imaging Vis. 48 (1) (2014)
Benoît Frénay, Daniela Hofmann, Alexander Schulz, Michael Biehl, Barbara Hammer
Valid interpretation of feature relevance for linear data mappings. CIDM (2014)
Herbert Teun Kruitbosch, Ioannis Giotis, Michael Biehl
Segmented shape-symbolic time series representation. ESANN (2014)
Mandy Lange, Michael Biehl, Thomas Villmann
Non-Euclidean independent component analysis and Oja's learning. ESANN (2013)
Enrique Alegre, Michael Biehl, Nicolai Petkov, Lidia Sánchez
Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ. Comput. Methods Programs Biomed. 111 (3) (2013)
Ioannis Giotis, Kerstin Bunte, Nicolai Petkov, Michael Biehl
Adaptive Matrices and Filters for Color Texture Classification. J. Math. Imaging Vis. 47 (1-2) (2013)
Michael Biehl, Barbara Hammer, Thomas Villmann
Distance Measures for Prototype Based Classification. BrainComp (2013)
Marc Strickert, Barbara Hammer, Thomas Villmann, Michael Biehl
Regularization and improved interpretation of linear data mappings and adaptive distance measures. CIDM (2013)
Bassam Mokbel, Wouter Lueks, Andrej Gisbrecht, Michael Biehl, Barbara Hammer
Visualizing the quality of dimensionality reduction. ESANN (2012)
Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann
Functional relevance learning in generalized learning vector quantization. Neurocomputing 90 (2012)
Michael Biehl, Marika Kästner, Mandy Lange, Thomas Villmann
Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects. WSOM (2012)
Michael Biehl
Admire LVQ - Adaptive Distance Measures in Relevance Learning Vector Quantization. Künstliche Intell. 26 (4) (2012)
Kerstin Bunte, Michael Biehl, Barbara Hammer
A General Framework for Dimensionality-Reducing Data Visualization Mapping. Neural Comput. 24 (3) (2012)
Kerstin Bunte, Frank-Michael Schleif, Michael Biehl
Adaptive learning for complex-valued data. ESANN (2012)
Michael Biehl, Petra Schneider, David Smith, Han Stiekema, Angela Taylor, Beverly Hughes, Cedric Shackleton, Paul Stewart, Wiebke Arlt
Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. ESANN (2012)
Kerstin Bunte, Sven Haase, Michael Biehl, Thomas Villmann
Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing 90 (2012)
Markus B. Huber, Kerstin Bunte, Mahesh B. Nagarajan, Michael Biehl, Lawrence A. Ray, Axel Wismüller
Texture feature ranking with relevance learning to classify interstitial lung disease patterns. Artif. Intell. Medicine 56 (2) (2012)
Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann
Visualization of processes in self-learning systems. PST (2012)
Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN (2012)
Marika Kästner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann
Differentiable Kernels in Generalized Matrix Learning Vector Quantization. ICMLA (1) (2012)
Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks 26 (2012)
Barbara Hammer, Michael Biehl, Kerstin Bunte, Bassam Mokbel
A General Framework for Dimensionality Reduction for Large Data Sets. WSOM (2011)
Kerstin Bunte, Michael Biehl, Barbara Hammer
Supervised dimension reduction mappings. ESANN (2011)

Michael Biehl.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Michael Biehl