Michael Biehl - RankMe

172 papers B 40C 10Journal 75Unranked 43

Year	Rank	Type	Title / 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
2026	J	`jnl`	Iterated relevance matrix analysis for improved classification and robustness in prototype-based learning schemes. Neurocomputing Sofie Lövdal, Elina L. van den Brandhof, Michael Biehl
2026	J	`jnl`	Leveraging ordinal generalized matrix learning vector quantization for improved classification. Neural Comput. Appl. Lida Abdi, Alessandro Prete, Wiebke Arlt, Michael Biehl
2025	J	`jnl`	Discriminative Subspace Emersion from learning feature relevances across different populations. CoRR Marco Canducci, Lida Abdi, Alessandro Prete, Roland J. Veen, Michael Biehl, Wiebke Arlt, Peter Tino
2025	J	`jnl`	Explainable machine learning for movement disorders - Classification of tremor and myoclonus. Comput. Biol. Medicine 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
2025	J	`jnl`	Hermite polynomials facilitating on-line learning analysis of layered neural networks with arbitrary activation function. Neurocomputing Otavio Citton, Frederieke Richert, Michael Biehl, Michiel Straat
2025	J	`jnl`	Interpretable modelling and visualization of biomedical data. Neurocomputing Sreejita Ghosh, Elizabeth Sarah Baranowski, Michael Biehl, Wiebke Arlt, Peter Tiño, Kerstin Bunte
2024	—	`conf`	A Graph-Theory Based fMRI Analysis. ICCS (6) Luca Barillaro, Marianna Milano, Maria Eugenia Caligiuri, Jelle R. Dalenberg, Giuseppe Agapito, Michael Biehl, Mario Cannataro
2024	J	`jnl`	Aligning Generalisation Between Humans and Machines. CoRR 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, Pasquale Minervini, Martin Mundt, Axel-Cyrille Ngonga Ngomo, Alessandro Oltramari, Gabriella Pasi, Zeynep G. Saribatur, Luciano Serafini, John Shawe-Taylor, Vered Shwartz, Gabriella Skitalinskaya, Clemens Stachl, Gido M. van de Ven, Thomas Villmann
2024	—	`conf`	Interpretable Machine Learning in Endocrinology: A Diagnostic Tool in Primary Aldosteronism. WSOM+ Michael Biehl, David Pavlov, Alice J. Sitch, Alessandro Prete, Wiebke Arlt
2024	B	`conf`	Interpreting Hybrid AI through Autodecoded Latent Space Entities. ESANN Roland Veen, Christodoulos Hadjichristodoulou, Michael Biehl
2024	J	`jnl`	Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces. CoRR Sofie Lövdal, Michael Biehl
2024	J	`jnl`	Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces. Neurocomputing Sofie Lövdal, Michael Biehl
2024	B	`conf`	On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions. ESANN Frederieke Richert, Otavio Citton, Michael Biehl
2024	J	`jnl`	Subspace corrected relevance learning with application in neuroimaging. Artif. Intell. Medicine 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
2023	B	`conf`	Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA). ESANN Michael Biehl, Sofie Lövdal
2023	B	`conf`	Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis. ESANN Frederieke Richert, Michiel Straat, Elisa Oostwal, Michael Biehl
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	J	`jnl`	A machine learning based approach to gravitational lens identification with the International LOFAR Telescope. CoRR Samira Rezaei, John P. McKean, Michael Biehl, W. de Roo, A. Lafontaine
2022	J	`jnl`	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. 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
2022	J	`jnl`	Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets. CoRR Sreejita Ghosh, Elizabeth Sarah Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte
2022	J	`jnl`	Supervised learning in the presence of concept drift: a modelling framework. Neural Comput. Appl. Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl
2021	J	`jnl`	DECORAS: detection and characterization of radio-astronomical sources using deep learning. CoRR Samira Rezaei, John P. McKean, Michael Biehl, Amir Javadpour
2021	J	`jnl`	Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases. IEEE Access Godliver Owomugisha, Friedrich Melchert, Ernest Mwebaze, John A. Quinn, Michael Biehl
2021	J	`jnl`	sklvq: Scikit Learning Vector Quantization. J. Mach. Learn. Res. Rick van Veen, Michael Biehl, Gert-Jan de Vries
2020	C	`conf`	A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field. COMPASS Godliver Owomugisha, Pius K. B. Mugagga, Friedrich Melchert, Ernest Mwebaze, John A. Quinn, Michael Biehl
2020	J	`jnl`	Adaptive basis functions for prototype-based classification of functional data. Neural Comput. Appl. Friedrich Melchert, Gabriele Bani, Udo Seiffert, Michael Biehl
2020	J	`jnl`	An application of generalized matrix learning vector quantization in neuroimaging. Comput. Methods Programs Biomed. 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
2020	—	`conf`	Complex-Valued Embeddings of Generic Proximity Data. S+SSPR Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
2020	J	`jnl`	Complex-valued embeddings of generic proximity data. CoRR Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
2020	J	`jnl`	Data-Driven Supervised Learning for Life Science Data. Frontiers Appl. Math. Stat. Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif
2020	—	`conf`	Early detection of plant diseases using spectral data. APPIS Godliver Owomugisha, Ephraim Nuwamanya, John A. Quinn, Michael Biehl, Ernest Mwebaze
2020	J	`jnl`	Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information. Neurocomputing Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tiño, Barbara Hammer
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	C	`conf`	Structure Preserving Encoding of Non-euclidean Similarity Data. ICPRAM Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif
2020	J	`jnl`	Supervised Learning in the Presence of Concept Drift: A modelling framework. CoRR Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl
2019	—	`conf`	A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle. CAIP (2) Amey Bhole, Owen Falzon, Michael Biehl, George Azzopardi
2019	J	`jnl`	Feature Relevance Bounds for Ordinal Regression. CoRR Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer
2019	J	`jnl`	Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. CoRR Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tiño, Barbara Hammer
2019	B	`conf`	Feature relevance bounds for ordinal regression. ESANN Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer
2019	J	`jnl`	Galaxy classification: A machine learning analysis of GAMA catalogue data. CoRR Aleke Nolte, Lingyu Wang, Maciej Bilicki, Benne Holwerda, Michael Biehl
2019	J	`jnl`	Galaxy classification: A machine learning analysis of GAMA catalogue data. Neurocomputing Aleke Nolte, Lingyu Wang, Maciej Bilicki, Benne Holwerda, Michael Biehl
2019	J	`jnl`	Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation. CoRR Elisa Oostwal, Michiel Straat, Michael Biehl
2019	B	`conf`	On-line learning dynamics of ReLU neural networks using statistical physics techniques. ESANN Michiel Straat, Michael Biehl
2019	J	`jnl`	On-line learning dynamics of ReLU neural networks using statistical physics techniques. CoRR Michiel Straat, Michael Biehl
2019	—	`conf`	Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. WSOM+ Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer
2019	J	`jnl`	Prototype-based classifiers in the presence of concept drift: A modelling framework. CoRR Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer
2019	B	`conf`	Statistical physics of learning and inference. ESANN Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann
2019	—	`conf`	The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios. BrainComp Michael Biehl
2018	J	`jnl`	Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing Fabio Aiolli, Michael Biehl, Luca Oneto
2018	J	`jnl`	Learning vector quantization classifiers for ROC-optimization. Comput. Stat. Thomas Villmann, Marika Kaden, Wieland Hermann, Michael Biehl
2018	—	`conf`	Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases. APPIS 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
2018	B	`conf`	Machine learning and data analysis in astroinformatics. ESANN Michael Biehl, Kerstin Bunte, Giuseppe Longo, Peter Tiño
2018	B	`conf`	Prototype-based analysis of GAMA galaxy catalogue data. ESANN Aleke Nolte, Lingyu Wang, Michael Biehl
2018	J	`jnl`	Statistical Mechanics of On-Line Learning Under Concept Drift. Entropy Michiel Straat, Fthi Abadi, Christina Göpfert, Barbara Hammer, Michael Biehl
2017	—	`conf`	Adaptive basis functions for prototype-based classification of functional data. WSOM Gabriele Bani, Udo Seiffert, Michael Biehl, Friedrich Melchert
2017	—	`conf`	Biomedical Applications of Prototype Based Classifiers and Relevance Learning. AlCoB Michael Biehl
2017	B	`conf`	Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. ESANN Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke
2017	B	`conf`	Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders. ESANN Sreejita Ghosh, Elizabeth Sarah Baranowski, Rick van Veen, Gert-Jan de Vries, Michael Biehl, Wiebke Arlt, Peter Tiño, Kerstin Bunte
2017	—	`conf`	Empirical evaluation of gradient methods for matrix learning vector quantization. WSOM Michael LeKander, Michael Biehl, Harm de Vries
2017	—	`conf`	Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. WSOM Thomas Villmann, Michael Biehl, Andrea Villmann, Sascha Saralajew
2017	B	`conf`	Marker selection for the detection of trisomy 21 using generalized matrix learning vector quantization. IJCNN Andreas C. Neocleous, Costas Neocleous, Christos N. Schizas, Michael Biehl, Nicolai Petkov
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	—	`conf`	Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. ICAISC (1) Mohammad Mohammadi, Michael Biehl, Andrea Villmann, 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	—	`conf`	Functional Representation of Prototypes in LVQ and Relevance Learning. WSOM Friedrich Melchert, Udo Seiffert, Michael Biehl
2016	J	`jnl`	Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261). Dagstuhl Reports Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke
2016	—	`conf`	LVQ and SVM Classification of FDG-PET Brain Data. WSOM Deborah Mudali, Michael Biehl, Klaus Leonard Leenders, Jos B. T. M. Roerdink
2016	J	`jnl`	Odor recognition in robotics applications by discriminative time-series modeling. Pattern Anal. Appl. Frank-Michael Schleif, Barbara Hammer, Javier Gonzalez Monroy, Javier González Jiménez, José Luis Blanco-Claraco, Michael Biehl, Nicolai Petkov
2016	B	`conf`	Predicting recurrence in clear cell Renal Cell Carcinoma: Analysis of TCGA data using outlier analysis and generalized matrix LVQ. CEC Gargi Mukherjee, Gyan Bhanot, Kevin Raines, Srikanth Sastry, Sebastian Doniach, Michael Biehl
2016	—	`conf`	Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis. WSOM Ernest Mwebaze, Michael Biehl
2016	—	`conf`	Prototype-based Models for the Supervised Learning of Classification Schemes. Astroinformatics Michael Biehl, Barbara Hammer, Thomas Villmann
2015	—	`conf`	Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ. BrainComp Michael Biehl, Deborah Mudali, Klaus Leonard Leenders, Jos B. T. M. Roerdink
2015	B	`conf`	Combining dissimilarity measures for prototype-based classification. ESANN Ernest Mwebaze, Gjalt Bearda, Michael Biehl, Dietlind Zühlke
2015	J	`jnl`	Developments in computational intelligence and machine learning. Neurocomputing Michael Biehl, Alessandro Ghio, Frank-Michael Schleif
2015	—	`conf`	Facial Expression Recognition Using Learning Vector Quantization. CAIP (2) Gert-Jan de Vries, Steffen Pauws, Michael Biehl
2015	—	`conf`	Inferring Feature Relevances From Metric Learning. SSCI Alexander Schulz, Bassam Mokbel, Michael Biehl, Barbara Hammer
2015	J	`jnl`	Insightful stress detection from physiology modalities using Learning Vector Quantization. Neurocomputing Gert-Jan de Vries, Steffen C. Pauws, Michael Biehl
2015	J	`jnl`	Inter-species inference of gene set enrichment in lung epithelial cells from proteomic and large transcriptomic datasets. Bioinform. Sahand Hormoz, Gyan Bhanot, Michael Biehl, Erhan Bilal, Pablo Meyer, Raquel Norel, Kahn Rhrissorrakrai, Adel Dayarian
2015	J	`jnl`	Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge. Bioinform. Michael Biehl, Peter J. Sadowski, Gyan Bhanot, Erhan Bilal, Adel Dayarian, Pablo Meyer, Raquel Norel, Kahn Rhrissorrakrai, Michael D. Zeller, Sahand Hormoz
2015	—	`conf`	Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. CAIP (2) Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl
2015	J	`jnl`	MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. Ioannis Giotis, Nynke Molders, Sander Land, Michael Biehl, Marcel F. Jonkman, Nicolai Petkov
2015	J	`jnl`	Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution. Frontiers Comput. Neurosci. Oltman Ottes de Wiljes, Ronald A. J. van Elburg, Michael Biehl, Fred Keijzer
2015	J	`jnl`	Non-Euclidean principal component analysis by Hebbian learning. Neurocomputing Mandy Lange, Michael Biehl, Thomas Villmann
2015	J	`jnl`	Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge. Bioinform. 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
2015	B	`conf`	Stationarity of Matrix Relevance LVQ. IJCNN Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
2015	J	`jnl`	Towards Emotion Classification Using Appraisal Modeling. Int. J. Synth. Emot. Gert-Jan de Vries, Paul Lemmens, Dirk Brokken, Steffen Pauws, Michael Biehl
2014	J	`jnl`	Erratum to: Adaptive Matrices and Filters for Color Texture Classification. J. Math. Imaging Vis. Ioannis Giotis, Kerstin Bunte, Nicolai Petkov, Michael Biehl
2014	—	`conf`	Prototype-Based Classifiers and Their Application in the Life Sciences. WSOM Michael Biehl
2014	B	`conf`	Segmented shape-symbolic time series representation. ESANN Herbert Teun Kruitbosch, Ioannis Giotis, Michael Biehl
2014	C	`conf`	Valid interpretation of feature relevance for linear data mappings. CIDM Benoît Frénay, Daniela Hofmann, Alexander Schulz, Michael Biehl, Barbara Hammer
2013	J	`jnl`	Adaptive Matrices and Filters for Color Texture Classification. J. Math. Imaging Vis. Ioannis Giotis, Kerstin Bunte, Nicolai Petkov, Michael Biehl
2013	J	`jnl`	Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ. Comput. Methods Programs Biomed. Enrique Alegre, Michael Biehl, Nicolai Petkov, Lidia Sánchez
2013	—	`conf`	Distance Measures for Prototype Based Classification. BrainComp Michael Biehl, Barbara Hammer, Thomas Villmann
2013	B	`conf`	Non-Euclidean independent component analysis and Oja's learning. ESANN Mandy Lange, Michael Biehl, Thomas Villmann
2013	C	`conf`	Regularization and improved interpretation of linear data mappings and adaptive distance measures. CIDM Marc Strickert, Barbara Hammer, Thomas Villmann, Michael Biehl
2012	J	`jnl`	A General Framework for Dimensionality-Reducing Data Visualization Mapping. Neural Comput. Kerstin Bunte, Michael Biehl, Barbara Hammer
2012	B	`conf`	Adaptive learning for complex-valued data. ESANN Kerstin Bunte, Frank-Michael Schleif, Michael Biehl
2012	J	`jnl`	Admire LVQ - Adaptive Distance Measures in Relevance Learning Vector Quantization. Künstliche Intell. Michael Biehl
2012	—	`conf`	Differentiable Kernels in Generalized Matrix Learning Vector Quantization. ICMLA (1) Marika Kästner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann
2012	J	`jnl`	Functional relevance learning in generalized learning vector quantization. Neurocomputing Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann
2012	B	`conf`	Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
2012	J	`jnl`	Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2012	B	`conf`	Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. ESANN Michael Biehl, Petra Schneider, David Smith, Han Stiekema, Angela Taylor, Beverly Hughes, Cedric Shackleton, Paul Stewart, Wiebke Arlt
2012	—	`conf`	Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects. WSOM Michael Biehl, Marika Kästner, Mandy Lange, Thomas Villmann
2012	J	`jnl`	Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing Kerstin Bunte, Sven Haase, Michael Biehl, Thomas Villmann
2012	J	`jnl`	Texture feature ranking with relevance learning to classify interstitial lung disease patterns. Artif. Intell. Medicine Markus B. Huber, Kerstin Bunte, Mahesh B. Nagarajan, Michael Biehl, Lawrence A. Ray, Axel Wismüller
2012	C	`conf`	Visualization of processes in self-learning systems. PST Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann
2012	B	`conf`	Visualizing the quality of dimensionality reduction. ESANN Bassam Mokbel, Wouter Lueks, Andrej Gisbrecht, Michael Biehl, Barbara Hammer
2011	—	`conf`	A General Framework for Dimensionality Reduction for Large Data Sets. WSOM Barbara Hammer, Michael Biehl, Kerstin Bunte, Bassam Mokbel
2011	—	`conf`	Adaptive Matrices for Color Texture Classification. CAIP (2) Kerstin Bunte, Ioannis Giotis, Nicolai Petkov, Michael Biehl
2011	B	`conf`	Causal relevance learning for robust classification under interventions. ESANN Ernest Mwebaze, John A. Quinn, Michael Biehl
2011	C	`conf`	Dimensionality reduction mappings. CIDM Kerstin Bunte, Michael Biehl, Barbara Hammer
2011	J	`jnl`	Divergence-based classification in learning vector quantization. Neurocomputing Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl
2011	B	`conf`	Generalized functional relevance learning vector quantization. ESANN Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann
2011	J	`jnl`	How to Evaluate Dimensionality Reduction? - Improving the Co-ranking Matrix CoRR Wouter Lueks, Bassam Mokbel, Michael Biehl, Barbara Hammer
2011	J	`jnl`	Learning effective color features for content based image retrieval in dermatology. Pattern Recognit. Kerstin Bunte, Michael Biehl, Marcel F. Jonkman, Nicolai Petkov
2011	J	`jnl`	Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). Dagstuhl Reports Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, Thomas Villmann
2011	B	`conf`	Learning of causal relations. ESANN John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl
2011	B	`conf`	Multivariate class labeling in Robust Soft LVQ. ESANN Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann
2011	J	`jnl`	Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller
2011	B	`conf`	Supervised dimension reduction mappings. ESANN Kerstin Bunte, Michael Biehl, Barbara Hammer
2011	—	`conf`	Texture feature selection with relevance learning to classify interstitial lung disease patterns. Computer-Aided Diagnosis Markus B. Huber, Kerstin Bunte, Mahesh B. Nagarajan, Michael Biehl, Lawrence A. Ray, Axel Wismüller
2010	J	`jnl`	Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data. Neurocomputing Kerstin Bunte, Barbara Hammer, Axel Wismüller, Michael Biehl
2010	B	`conf`	Divergence based Learning Vector Quantization. ESANN Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl
2010	C	`conf`	Early Nervous Systems - Theoretical Background and a Preliminary Model of Neuronal Processes. ALIFE Ot de Wiljes, Ronald A. J. van Elburg, Michael Biehl, Fred Keijzer
2010	B	`conf`	Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. ESANN Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller
2010	C	`conf`	Generalized Derivative Based Kernelized Learning Vector Quantization. IDEAL Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl
2010	J	`jnl`	Hyperparameter learning in probabilistic prototype-based models. Neurocomputing Petra Schneider, Michael Biehl, Barbara Hammer
2010	J	`jnl`	Regularization in matrix relevance learning. IEEE Trans. Neural Networks Petra Schneider, Kerstin Bunte, Han Stiekema, Barbara Hammer, Thomas Villmann, Michael Biehl
2010	—	`conf`	Some steps towards a general principle for dimensionality reduction mappings. Learning paradigms in dynamic environments Barbara Hammer, Kerstin Bunte, Michael Biehl
2010	—	`conf`	The Mathematics of Divergence Based Online Learning in Vector Quantization. ANNPR Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl
2010	J	`jnl`	Window-Based Example Selection in Learning Vector Quantization. Neural Comput. Aree Witoelar, Anarta Ghosh, J. J. G. de Vries, Barbara Hammer, Michael Biehl
2009	—	`conf`	09081 Abstracts Collection - Similarity-based learning on structures. Similarity-based learning on structures Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann
2009	—	`conf`	09081 Summary - Similarity-based learning on structures. Similarity-based learning on structures Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann
2009	B	`conf`	Adaptive Metrics for Content Based Image Retrieval in Dermatology. ESANN Kerstin Bunte, Michael Biehl, Nicolai Petkov, Marcel F. Jonkman
2009	J	`jnl`	Adaptive Relevance Matrices in Learning Vector Quantization. Neural Comput. Petra Schneider, Michael Biehl, Barbara Hammer
2009	J	`jnl`	Advances in machine learning and computational intelligence. Neurocomputing Frank-Michael Schleif, Michael Biehl, Alfredo Vellido
2009	—	`conf`	Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition. Similarity-based learning on structures Gert-Jan de Vries, Michael Biehl
2009	J	`jnl`	Distance Learning in Discriminative Vector Quantization. Neural Comput. Petra Schneider, Michael Biehl, Barbara Hammer
2009	B	`conf`	Equilibrium properties of off-line LVQ. ESANN Aree Witoelar, Michael Biehl, Barbara Hammer
2009	B	`conf`	Hyperparameter Learning in Robust Soft LVQ. ESANN Petra Schneider, Michael Biehl, Barbara Hammer
2009	—	`conf`	Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. IWANN (1) Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2009	—	`ch.`	Metric Learning for Prototype-Based Classification. Innovations in Neural Information Paradigms and Applications Michael Biehl, Barbara Hammer, Petra Schneider, Thomas Villmann
2009	C	`conf`	Nonlinear Dimension Reduction and Visualization of Labeled Data. CAIP Kerstin Bunte, Barbara Hammer, Michael Biehl
2009	B	`conf`	Nonlinear Discriminative Data Visualization. ESANN Kerstin Bunte, Barbara Hammer, Petra Schneider, Michael Biehl
2009	J	`jnl`	Phase transitions in vector quantization and neural gas. Neurocomputing Aree Witoelar, Michael Biehl
2009	—	`ed.`	Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar] Similarity-Based Clustering Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann
2009	—	`ed.`	Similarity-based learning on structures, 15.02. - 20.02.2009 Similarity-based learning on structures Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann
2009	—	`conf`	Some Theoretical Aspects of the Neural Gas Vector Quantizer. Similarity-Based Clustering Thomas Villmann, Barbara Hammer, Michael Biehl
2009	—	`conf`	Statistical Mechanics of On-line Learning. Similarity-Based Clustering Michael Biehl, Nestor Caticha, Peter Riegler
2008	J	`jnl`	Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. Comput. Biol. Medicine Enrique Alegre, Michael Biehl, Nicolai Petkov, Lidia Sánchez
2008	—	`conf`	Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. ANNPR Marc Strickert, Petra Schneider, Jens Keilwagen, Thomas Villmann, Michael Biehl, Barbara Hammer
2008	B	`conf`	Generalized matrix learning vector quantizer for the analysis of spectral data. ESANN Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2008	J	`jnl`	Learning dynamics and robustness of vector quantization and neural gas. Neurocomputing Aree Witoelar, Michael Biehl, Anarta Ghosh, Barbara Hammer
2008	B	`conf`	Phase transitions in Vector Quantization. ESANN Aree Witoelar, Anarta Ghosh, Michael Biehl
2008	J	`jnl`	Progress in modeling, theory, and application of computational intelligence. Neurocomputing Fabrice Rossi, Michael Biehl, Cecilio Angulo Bahón
2007	—	`conf`	07131 Abstracts Collection -- Similarity-based Clustering and its Application to Medicine and Biology. Similarity-based Clustering and its Application to Medicine and Biology Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann
2007	—	`conf`	07131 Summary -- Similarity-based Clustering and its Application to Medicine and Biology. Similarity-based Clustering and its Application to Medicine and Biology Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann
2007	J	`jnl`	Advances in computational intelligence and learning. Neurocomputing Michael Biehl, Erzsébet Merényi, Fabrice Rossi
2007	C	`conf`	Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning. IDEAL Michael Biehl, Rainer Breitling, Yang Li
2007	J	`jnl`	Dynamics and Generalization Ability of LVQ Algorithms. J. Mach. Learn. Res. Michael Biehl, Anarta Ghosh, Barbara Hammer
2007	—	`conf`	Learning Vector Quantization: generalization ability and dynamics of competing prototypes. Similarity-based Clustering and its Application to Medicine and Biology Aree Witoelar, Michael Biehl, Barbara Hammer
2007	B	`conf`	On the dynamics of Vector Quantization and Neural Gas. ESANN Aree Witoelar, Michael Biehl, Anarta Ghosh, Barbara Hammer
2007	B	`conf`	Relevance matrices in LVQ. ESANN Petra Schneider, Michael Biehl, Barbara Hammer
2007	—	`ed.`	Similarity-based Clustering and its Application to Medicine and Biology, 25.03. - 30.03.2007 Similarity-based Clustering and its Application to Medicine and Biology Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann
2006	B	`conf`	Classification of Boar Sperm Head Images using Learning Vector Quantization. ESANN Michael Biehl, Piter Pasma, Marten Pijl, Lidia Sánchez, Nicolai Petkov
2006	—	`conf`	LVQ acrosome integrity assessment of boar sperm cells. CompIMAGE Nikolai Petkov, Enrique Alegre, Michael Biehl, Lidia Sánchez
2006	J	`jnl`	Learning vector quantization: The dynamics of winner-takes-all algorithms. Neurocomputing Michael Biehl, Anarta Ghosh, Barbara Hammer
2006	J	`jnl`	Performance analysis of LVQ algorithms: A statistical physics approach. Neural Networks Anarta Ghosh, Michael Biehl, Barbara Hammer
2005	B	`conf`	The dynamics of Learning Vector Quantization. ESANN Michael Biehl, Anarta Ghosh, Barbara Hammer
2002	B	`conf`	Supervised learning in committee machines by PCA. ESANN Christoph Bunzmann, Michael Biehl, Robert Urbanczik