Michael Biehl

According to our database, Michael Biehl authored at least 145 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces.

Sofie Lövdal, Michael Biehl

CoRR, 2024

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

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

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

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

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

Proceedings of the Advances in Intelligent Data Analysis XX, 2022

2021

sklvq: Scikit Learning Vector Quantization.

Rick van Veen, Michael Biehl, Gert-Jan de Vries

J. Mach. Learn. Res., 2021

DECORAS: detection and characterization of radio-astronomical sources using deep learning.

Samira Rezaei, John P. McKean, Michael Biehl, Amir Javadpour

CoRR, 2021

Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases.

Godliver Owomugisha, Friedrich Melchert, Ernest Mwebaze, John A. Quinn, Michael Biehl

IEEE Access, 2021

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

Adaptive basis functions for prototype-based classification of functional data.

Friedrich Melchert, Gabriele Bani, Udo Seiffert, Michael Biehl

Neural Comput. Appl., 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

Data-Driven Supervised Learning for Life Science Data.

Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif

Frontiers Appl. Math. Stat., 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, Michael Biehl

Comput. Methods Programs Biomed., 2020

Complex-Valued Embeddings of Generic Proximity Data.

Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2020

Structure Preserving Encoding of Non-euclidean Similarity Data.

Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif

Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, 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

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies, 2020

Early detection of plant diseases using spectral data.

Godliver Owomugisha, Ephraim Nuwamanya, John A. Quinn, Michael Biehl, Ernest Mwebaze

Proceedings of the APPIS 2020: 3rd International Conference on Applications of Intelligent Systems, 2020

2019

Galaxy classification: A machine learning analysis of GAMA catalogue data.

Aleke Nolte, Lingyu Wang, Maciej Bilicki, Benne Holwerda, Michael Biehl

Neurocomputing, 2019

Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation.

Elisa Oostwal, Michiel Straat, Michael Biehl

CoRR, 2019

Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework.

Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer

Proceedings of the Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, 2019

On-line learning dynamics of ReLU neural networks using statistical physics techniques.

Michiel Straat, Michael Biehl

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Feature relevance bounds for ordinal regression.

Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Statistical physics of learning and inference.

Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann

Proceedings of the 27th European Symposium on Artificial Neural Networks, 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

Proceedings of the Computer Analysis of Images and Patterns, 2019

The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios.

Michael Biehl

Proceedings of the Brain-Inspired Computing - 4th International Workshop, 2019

2018

Advances in artificial neural networks, machine learning and computational intelligence.

Fabio Aiolli, Michael Biehl, Luca Oneto

Neurocomputing, 2018

Statistical Mechanics of On-Line Learning Under Concept Drift.

Michiel Straat, Fthi Abadi, Christina Göpfert, Barbara Hammer, Michael Biehl

Entropy, 2018

Learning vector quantization classifiers for ROC-optimization.

Thomas Villmann, Marika Kaden, Wieland Hermann, Michael Biehl

Comput. Stat., 2018

Prototype-based analysis of GAMA galaxy catalogue data.

Aleke Nolte, Lingyu Wang, Michael Biehl

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Machine learning and data analysis in astroinformatics.

Michael Biehl, Kerstin Bunte, Giuseppe Longo, Peter Tiño

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

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, J. J. G. de Vries, Michael Biehl

Proceedings of the Applications of Intelligent Systems, 2018

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

Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 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

Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 2017

Empirical evaluation of gradient methods for matrix learning vector quantization.

Michael LeKander, Michael Biehl, Harm de Vries

Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 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

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices.

Mohammad Mohammadi, Michael Biehl, Andrea Villmann, Thomas Villmann

Proceedings of the Artificial Intelligence and Soft Computing, 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

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Biomedical data analysis in translational research: integration of expert knowledge and interpretable models.

Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Biomedical Applications of Prototype Based Classifiers and Relevance Learning.

Michael Biehl

Proceedings of the Algorithms for Computational Biology, 2017

2016

Odor recognition in robotics applications by discriminative time-series modeling.

Frank-Michael Schleif, Barbara Hammer, Javier Gonzalez Monroy, Javier González Jiménez, José-Luis Blanco-Claraco, Michael Biehl, Nicolai Petkov

Pattern Anal. Appl., 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

Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis.

Ernest Mwebaze, Michael Biehl

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2016

LVQ and SVM Classification of FDG-PET Brain Data.

Deborah Mudali, Michael Biehl, Klaus Leonard Leenders, Jos B. T. M. Roerdink

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2016

Functional Representation of Prototypes in LVQ and Relevance Learning.

Friedrich Melchert, Udo Seiffert, Michael Biehl

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2016

Complex Variants of GLVQ Based on Wirtinger's Calculus.

Matthias Gay, Marika Kaden, Michael Biehl, Alexander Lampe, Thomas Villmann

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 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

Proceedings of the IEEE Congress on Evolutionary Computation, 2016

Prototype-based Models for the Supervised Learning of Classification Schemes.

Michael Biehl, Barbara Hammer, Thomas Villmann

Proceedings of the Astroinformatics 2016, Sorrento, Italy, October 19-25, 2016, 2016

2015

Towards Emotion Classification Using Appraisal Modeling.

Gert-Jan de Vries, Paul Lemmens, Dirk Brokken, Steffen Pauws, Michael Biehl

Int. J. Synth. Emot., 2015

Insightful stress detection from physiology modalities using Learning Vector Quantization.

Gert-Jan de Vries, Steffen C. Pauws, Michael Biehl

Neurocomputing, 2015

Non-Euclidean principal component analysis by Hebbian learning.

Mandy Lange, Michael Biehl, Thomas Villmann

Neurocomputing, 2015

Developments in computational intelligence and machine learning.

Michael Biehl, Alessandro Ghio, Frank-Michael Schleif

Neurocomputing, 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

MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images.

Ioannis Giotis, Nynke Molders, Sander Land, Michael Biehl, Marcel F. Jonkman, Nicolai Petkov

Expert Syst. Appl., 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, Raquel Norel, Kahn Rhrissorrakrai, Adel Dayarian

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, Raquel Norel, Kahn Rhrissorrakrai, Gyan Bhanot, Feng Luo, Adi L. Tarca

Bioinform., 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, Raquel Norel, Kahn Rhrissorrakrai, Michael D. Zeller, Sahand Hormoz

Bioinform., 2015

Inferring Feature Relevances From Metric Learning.

Alexander Schulz, Bassam Mokbel, Michael Biehl, Barbara Hammer

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015

Stationarity of Matrix Relevance LVQ.

Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Combining dissimilarity measures for prototype-based classification.

Ernest Mwebaze, Gjalt Bearda, Michael Biehl, Dietlind Zühlke

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

Facial Expression Recognition Using Learning Vector Quantization.

Gert-Jan de Vries, Steffen Pauws, Michael Biehl

Proceedings of the Computer Analysis of Images and Patterns, 2015

Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection.

Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl

Proceedings of the Computer Analysis of Images and Patterns, 2015

Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ.

Michael Biehl, Deborah Mudali, Klaus Leonard Leenders, Jos B. T. M. Roerdink

Proceedings of the Brain-Inspired Computing - Second International Workshop, 2015

2014

Erratum to: Adaptive Matrices and Filters for Color Texture Classification.

Ioannis Giotis, Kerstin Bunte, Nicolai Petkov, Michael Biehl

J. Math. Imaging Vis., 2014

Prototype-Based Classifiers and Their Application in the Life Sciences.

Michael Biehl

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2014

Segmented shape-symbolic time series representation.

Herbert Teun Kruitbosch, Ioannis Giotis, Michael Biehl

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

Valid interpretation of feature relevance for linear data mappings.

Benoît Frénay, Daniela Hofmann, Alexander Schulz, Michael Biehl, Barbara Hammer

Proceedings of the 2014 IEEE Symposium on Computational Intelligence and Data Mining, 2014

2013

Adaptive Matrices and Filters for Color Texture Classification.

Ioannis Giotis, Kerstin Bunte, Nicolai Petkov, Michael Biehl

J. Math. Imaging Vis., 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

Non-Euclidean independent component analysis and Oja's learning.

Mandy Lange, Michael Biehl, Thomas Villmann

Proceedings of the 21st European Symposium on Artificial Neural Networks, 2013

Regularization and improved interpretation of linear data mappings and adaptive distance measures.

Marc Strickert, Barbara Hammer, Thomas Villmann, Michael Biehl

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2013

Distance Measures for Prototype Based Classification.

Michael Biehl, Barbara Hammer, Thomas Villmann

Proceedings of the Brain-Inspired Computing - International Workshop, 2013

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

A General Framework for Dimensionality-Reducing Data Visualization Mapping.

Kerstin Bunte, Michael Biehl, Barbara Hammer

Neural Comput., 2012

Admire LVQ - Adaptive Distance Measures in Relevance Learning Vector Quantization.

Michael Biehl

Künstliche Intell., 2012

Functional relevance learning in generalized learning vector quantization.

Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann

Neurocomputing, 2012

Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences.

Kerstin Bunte, Sven Haase, Michael Biehl, Thomas Villmann

Neurocomputing, 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

Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects.

Michael Biehl, Marika Kästner, Mandy Lange, Thomas Villmann

Proceedings of the Advances in Self-Organizing Maps - 9th International Workshop, 2012

Visualization of processes in self-learning systems.

Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann

Proceedings of the Tenth Annual International Conference on Privacy, Security and Trust, 2012

Large margin linear discriminative visualization by Matrix Relevance Learning.

Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann

Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

Differentiable Kernels in Generalized Matrix Learning Vector Quantization.

Marika Kästner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

Visualizing the quality of dimensionality reduction.

Bassam Mokbel, Wouter Lueks, Andrej Gisbrecht, Michael Biehl, Barbara Hammer

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

Adaptive learning for complex-valued data.

Kerstin Bunte, Frank-Michael Schleif, Michael Biehl

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

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

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011

Learning effective color features for content based image retrieval in dermatology.

Kerstin Bunte, Michael Biehl, Marcel F. Jonkman, Nicolai Petkov

Pattern Recognit., 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

Neighbor embedding XOM for dimension reduction and visualization.

Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller

Neurocomputing, 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

How to Evaluate Dimensionality Reduction? - Improving the Co-ranking Matrix

Wouter Lueks, Bassam Mokbel, Michael Biehl, Barbara Hammer

CoRR, 2011

A General Framework for Dimensionality Reduction for Large Data Sets.

Barbara Hammer, Michael Biehl, Kerstin Bunte, Bassam Mokbel

Proceedings of the Advances in Self-Organizing Maps - 8th International Workshop, 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

Proceedings of the Medical Imaging 2011: Computer-Aided Diagnosis, 2011

Multivariate class labeling in Robust Soft LVQ.

Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Learning of causal relations.

John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Causal relevance learning for robust classification under interventions.

Ernest Mwebaze, John A. Quinn, Michael Biehl

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Generalized functional relevance learning vector quantization.

Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Supervised dimension reduction mappings.

Kerstin Bunte, Michael Biehl, Barbara Hammer

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Dimensionality reduction mappings.

Kerstin Bunte, Michael Biehl, Barbara Hammer

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2011

Adaptive Matrices for Color Texture Classification.

Kerstin Bunte, Ioannis Giotis, Nicolai Petkov, Michael Biehl

Proceedings of the Computer Analysis of Images and Patterns, 2011

2010

Regularization in matrix relevance learning.

Petra Schneider, Kerstin Bunte, Han Stiekema, Barbara Hammer, Thomas Villmann, Michael Biehl

IEEE Trans. Neural Networks, 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

Hyperparameter learning in probabilistic prototype-based models.

Petra Schneider, Michael Biehl, Barbara Hammer

Neurocomputing, 2010

Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data.

Kerstin Bunte, Barbara Hammer, Axel Wismüller, Michael Biehl

Neurocomputing, 2010

Generalized Derivative Based Kernelized Learning Vector Quantization.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl

Proceedings of the Intelligent Data Engineering and Automated Learning, 2010

Divergence based Learning Vector Quantization.

Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.

Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

Some steps towards a general principle for dimensionality reduction mappings.

Barbara Hammer, Kerstin Bunte, Michael Biehl

Proceedings of the Learning paradigms in dynamic environments, 25.07. - 30.07.2010, 2010

The Mathematics of Divergence Based Online Learning in Vector Quantization.

Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2010

Early Nervous Systems - Theoretical Background and a Preliminary Model of Neuronal Processes.

Ot de Wiljes, Ronald A. J. van Elburg, Michael Biehl, Fred Keijzer

Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems, 2010

2009

Metric Learning for Prototype-Based Classification.

Michael Biehl, Barbara Hammer, Petra Schneider, Thomas Villmann

Proceedings of the Innovations in Neural Information Paradigms and Applications, 2009

Adaptive Relevance Matrices in Learning Vector Quantization.

Petra Schneider, Michael Biehl, Barbara Hammer

Neural Comput., 2009

Distance Learning in Discriminative Vector Quantization.

Petra Schneider, Michael Biehl, Barbara Hammer

Neural Comput., 2009

Phase transitions in vector quantization and neural gas.

Aree Witoelar, Michael Biehl

Neurocomputing, 2009

Advances in machine learning and computational intelligence.

Frank-Michael Schleif, Michael Biehl, Alfredo Vellido

Neurocomputing, 2009

Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data.

Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl

Proceedings of the Bio-Inspired Systems: Computational and Ambient Intelligence, 2009

Equilibrium properties of off-line LVQ.

Aree Witoelar, Michael Biehl, Barbara Hammer

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

Hyperparameter Learning in Robust Soft LVQ.

Petra Schneider, Michael Biehl, Barbara Hammer

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

Nonlinear Discriminative Data Visualization.

Kerstin Bunte, Barbara Hammer, Petra Schneider, Michael Biehl

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

Adaptive Metrics for Content Based Image Retrieval in Dermatology.

Kerstin Bunte, Michael Biehl, Nicolai Petkov, Marcel F. Jonkman

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition.

Gert-Jan de Vries, Michael Biehl

Proceedings of the Similarity-based learning on structures, 15.02. - 20.02.2009, 2009

Some Theoretical Aspects of the Neural Gas Vector Quantizer.

Thomas Villmann, Barbara Hammer, Michael Biehl

Proceedings of the Similarity-Based Clustering, 2009

09081 Summary - Similarity-based learning on structures.

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann

Proceedings of the Similarity-based learning on structures, 15.02. - 20.02.2009, 2009

09081 Abstracts Collection - Similarity-based learning on structures.

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann

Proceedings of the Similarity-based learning on structures, 15.02. - 20.02.2009, 2009

Statistical Mechanics of On-line Learning.

Michael Biehl, Nestor Caticha, Peter Riegler

Proceedings of the Similarity-Based Clustering, 2009

Nonlinear Dimension Reduction and Visualization of Labeled Data.

Kerstin Bunte, Barbara Hammer, Michael Biehl

Proceedings of the Computer Analysis of Images and Patterns, 13th International Conference, 2009

2008

Learning dynamics and robustness of vector quantization and neural gas.

Aree Witoelar, Michael Biehl, Anarta Ghosh, Barbara Hammer

Neurocomputing, 2008

Progress in modeling, theory, and application of computational intelligence.

Fabrice Rossi, Michael Biehl, Cecilio Angulo Bahón

Neurocomputing, 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

Phase transitions in Vector Quantization.

Aree Witoelar, Anarta Ghosh, Michael Biehl

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

Generalized matrix learning vector quantizer for the analysis of spectral data.

Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.

Marc Strickert, Petra Schneider, Jens Keilwagen, Thomas Villmann, Michael Biehl, Barbara Hammer

Proceedings of the Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop, 2008

2007

Dynamics and Generalization Ability of LVQ Algorithms.

Michael Biehl, Anarta Ghosh, Barbara Hammer

J. Mach. Learn. Res., 2007

Advances in computational intelligence and learning.

Michael Biehl, Erzsébet Merényi, Fabrice Rossi

Neurocomputing, 2007

Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning.

Michael Biehl, Rainer Breitling, Yang Li

Proceedings of the Intelligent Data Engineering and Automated Learning, 2007

On the dynamics of Vector Quantization and Neural Gas.

Aree Witoelar, Michael Biehl, Anarta Ghosh, Barbara Hammer

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

Relevance matrices in LVQ.

Petra Schneider, Michael Biehl, Barbara Hammer

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

Learning Vector Quantization: generalization ability and dynamics of competing prototypes.

Aree Witoelar, Michael Biehl, Barbara Hammer

Proceedings of the Similarity-based Clustering and its Application to Medicine and Biology, 25.03., 2007

07131 Abstracts Collection -- Similarity-based Clustering and its Application to Medicine and Biology.

Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann

Proceedings of the Similarity-based Clustering and its Application to Medicine and Biology, 25.03., 2007

07131 Summary -- Similarity-based Clustering and its Application to Medicine and Biology.

Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann

Proceedings of the Similarity-based Clustering and its Application to Medicine and Biology, 25.03., 2007

2006

Performance analysis of LVQ algorithms: A statistical physics approach.

Anarta Ghosh, Michael Biehl, Barbara Hammer

Neural Networks, 2006

Learning vector quantization: The dynamics of winner-takes-all algorithms.

Michael Biehl, Anarta Ghosh, Barbara Hammer

Neurocomputing, 2006

Classification of Boar Sperm Head Images using Learning Vector Quantization.

Michael Biehl, Piter Pasma, Marten Pijl, Lidia Sánchez, Nicolai Petkov

Proceedings of the 14th European Symposium on Artificial Neural Networks, 2006

LVQ acrosome integrity assessment of boar sperm cells.

Nikolai Petkov, Enrique Alegre, Michael Biehl, Lidia Sánchez

Proceedings of the Computational Modeling of Objects Represented in Images-Fundamentals, 2006

2005

The dynamics of Learning Vector Quantization.

Michael Biehl, Anarta Ghosh, Barbara Hammer

Proceedings of the 13th European Symposium on Artificial Neural Networks, 2005

2002

Supervised learning in committee machines by PCA.

Christoph Bunzmann, Michael Biehl, Robert Urbanczik

Proceedings of the 10th Eurorean Symposium on Artificial Neural Networks, 2002