Michael Biehl

Thomas Villmann, Michael Biehl, Andrea Villmann, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

Discriminative visualization by limited rank matrix learning

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2008 .

Hyperparameter learning in probabilistic prototype-based models

Michael Biehl, Barbara Hammer, Petra Schneider, 2010, Neurocomputing.

Structure Preserving Encoding of Non-euclidean Similarity Data

Michael Biehl, Frank-Michael Schleif, Christoph Raab, 2020, ICPRAM.

Neighbor embedding XOM for dimension reduction and visualization

Thomas Villmann, Axel Wismüller, Michael Biehl, 2011, Neurocomputing.

Statistical Quality Measures and ROC-Optimization by Learning Vector Quantization Classifiers

Thomas Villmann, Michael Biehl, Marika Kaden, 2014 .

Workshop New Challenges in Neural Computation

Michael Biehl, Barbara Hammer, Bassam Mokbel, 2011 .

Advances in computational intelligence and learning

Erzsébet Merényi, Michael Biehl, Fabrice Rossi, 2007, Neurocomputing.

On-line Learning of Prototypes and Principal Components

Michael Biehl, Ansgar Freking, Mattias Hölzer, 1998 .

Noise robustness in multilayer neural networks

Michael Biehl, Nestor Caticha, Mauro Copelli, 1997 .

Phase transitions in Vector Quantization

Michael Biehl, Aree Witoelar, Anarta Ghosh, 2008, ESANN.

Texture feature selection with relevance learning to classify interstitial lung disease patterns

Axel Wismüller, Lawrence A. Ray, Michael Biehl, 2011, Medical Imaging.

Prototype-based models in machine learning.

Thomas Villmann, Michael Biehl, Barbara Hammer, 2016, Wiley interdisciplinary reviews. Cognitive science.

Adaptive Relevance Matrices in Learning Vector Quantization

Michael Biehl, Barbara Hammer, Petra Schneider, 2009, Neural Computation.

A General Framework for Dimensionality Reduction for Large Data Sets

Michael Biehl, Barbara Hammer, Bassam Mokbel, 2011, WSOM.

Odor recognition in robotics applications by discriminative time-series modeling

Javier Gonzalez Monroy, Javier González, Michael Biehl, 2015, Pattern Analysis and Applications.

18th European Symposium on Artificial Neural Networks, ESANN 2010, Bruges, Belgium, April 28-30, 2010, Proceedings

Thomas Villmann, Axel Wismüller, Michael Biehl, 2010, The European Symposium on Artificial Neural Networks.

Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge

Feng Luo, Gyan Bhanot, Michael Biehl, 2014, Bioinform..

Proc. 5th Intl. Workshop on Self-Organising Maps (WSOM 2005)

Michael Biehl, Barbara Hammer, Anarta Ghosh, 2005 .

Causal relevance learning for robust classification under interventions

Michael Biehl, Ernest Mwebaze, John A. Quinn, 2011, ESANN.

Divergence based Learning Vector Quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2010, ESANN.

Proc. 5th International Workshop on Computational Systems Biology

Michael Biehl, T. Villmann, Marc Strickert, 2008 .

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

Michael Biehl, Barbara Hammer, Christina Göpfert, 2019, WSOM+.

Prototype-Based Classifiers and Their Application in the Life Sciences

Michael Biehl, 2014, WSOM.

European Symposium on Artificial Neural Networks (ESANN) 2015

Michael Biehl, Ernest Mwebaze, Gjalt Bearda, 2015 .

On the dynamics of Vector Quantization and Neural Gas

Michael Biehl, Barbara Hammer, Aree Witoelar, 2007, ESANN.

Early detection of plant diseases using spectral data

Michael Biehl, Ernest Mwebaze, Godliver Owomugisha, 2020, APPIS.

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

Michael Biehl, Michiel Straat, Michael Biehl, 2019, ESANN.

Nonlinear Discriminative Data Visualization

Michael Biehl, Barbara Hammer, Petra Schneider, 2009, ESANN.

Non-Euclidean principal component analysis by Hebbian learning

Thomas Villmann, Michael Biehl, Mandy Lange, 2015, Neurocomputing.

An application of generalized matrix learning vector quantization in neuroimaging

Michael Biehl, Remco J Renken, Dario Arnaldi, 2020, Comput. Methods Programs Biomed..

Biomedical Applications of Prototype Based Classifiers and Relevance Learning

Michael Biehl, Michael Biehl, 2017, AlCoB.

Extending RSLVQ to handle data points with uncertain class assignments

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2009 .

Visualization of processes in self-learning systems

Thomas Villmann, Michael Biehl, Marc Strickert, 2012, 2012 Tenth Annual International Conference on Privacy, Security and Trust.

Empirical evaluation of gradient methods for matrix learning vector quantization

Michael Biehl, Harm de Vries, Michael LeKander, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ

Nicolai Petkov, Michael Biehl, Enrique Alegre, 2008, Comput. Biol. Medicine.

Functional relevance learning in generalized learning vector quantization

Thomas Villmann, Michael Biehl, Barbara Hammer, 2012, Neurocomputing.

10th Int. Work-Conference on Artificial Neural Networks (IWANN 2009)

Michael Biehl, Frank-Michael Schleif, T. Villmann, 2009 .

Classification of Boar Sperm Head Images using Learning Vector Quantization

Nicolai Petkov, Michael Biehl, Lidia Sánchez, 2006, ESANN.

Dropout in Learning Vector Quantization Networks for Regularized Learning and Classification Confidence Estimation

Thomas Villmann, Michael Biehl, Sascha Saralajew, 2018 .

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

Thomas Villmann, Gyan Bhanot, Michael Biehl, 2017, ESANN 2017.

MiWoCI Workshop-2013

Michael Biehl, Michael Biehl, 2013 .

Generalized Derivative Based Kernelized Learning Vector Quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2010, IDEAL.

Hyperparameter Learning in Robust Soft LVQ

Michael Biehl, Barbara Hammer, Petra Schneider, 2009, ESANN.

Relevance matrices in LVQ

Michael Biehl, Barbara Hammer, Petra Schneider, 2007, ESANN.

Proc. Workshop on Neural Networks: The Statistical Mechanics Perspective

Michael Biehl, M. Opper, 1995 .

Statistical Mechanics of On-line Learning

Michael Biehl, Nestor Caticha, Peter Riegler, 2009, Similarity-Based Clustering.

MIWOCI Workshop 2014

Michael Biehl, Barbara Hammer, Daniela Hofmann, 2014 .

LVQ acrosome integrity assessment of boar sperm cells

Nicolai Petkov, Michael Biehl, Enrique Alegre, 2006, CompIMAGE.

Equilibrium properties of off-line LVQ

Michael Biehl, Barbara Hammer, Aree Witoelar, 2009, ESANN.

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

Yang Li, Michael Biehl, Rainer Breitling, 2007, IDEAL.

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

Michael Biehl, Barbara Hammer, Peter Tino, 2020, Neurocomputing.

Prototype-based Models for the Supervised Learning of Classification Schemes

Thomas Villmann, Michael Biehl, Barbara Hammer, 2016, Astroinformatics.

Adaptive Matrices and Filters for Color Texture Classification

Nicolai Petkov, Michael Biehl, Ioannis Giotis, 2012, Journal of Mathematical Imaging and Vision.

The sugar dataset : A multimodal hyperspectral dataset for classification and research

Andrea Matros, Michael Biehl, Friedrich Melchert, 2016 .

Proceedings of the 22. European Symposium on Artificial Neural Networks ESANN

Michael Biehl, Ioannis Giotis, Herbert Teun Kruitbosch, 2014 .

Large margin linear discriminative visualization by Matrix Relevance Learning

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

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

Michael Biehl, Barbara Hammer, Bassam Mokbel, 2011, ArXiv.

09081 Summary - Similarity-based learning on structures

Thomas Villmann, Michael Biehl, Barbara Hammer, 2009, Similarity-based learning on structures.

Differentiable Kernels in Generalized Matrix Learning Vector Quantization

Thomas Villmann, Michael Biehl, Martin Riedel, 2012, 2012 11th International Conference on Machine Learning and Applications.

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

Michael Biehl, Gert-Jan de Vries, 2009, Similarity-based learning on structures.

Learning dynamics and robustness of vector quantization and neural gas

Michael Biehl, Barbara Hammer, Aree Witoelar, 2008, Neurocomputing.

Nonlinear Dimension Reduction and Visualization of Labeled Data

Michael Biehl, Barbara Hammer, Kerstin Bunte, 2009, CAIP.

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

Thomas Villmann, Michael Biehl, Marika Kaden, 2012, WSOM.

Parallel processing in neural networks and computers

Michael Biehl, J. K. Anlauf, 1990 .

Stationarity of Matrix Relevance LVQ

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Proc. 7th Italian Workshop on Neural Networks WIRN 1995

Michael Biehl, Sara A. Solla, Carmela Marangi, 1996 .

University of Groningen Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases

Remco J. Renken, Michael Biehl, Jos B. T. M. Roerdink, 2018 .

A process for the power supply of a computer, computer and a separate component for connection to a computer

Michael Biehl, Hans-Dieter Teuteberg, 2009 .

Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors

Michael Biehl, Paul Stewart, Petra Schneider, 2012, ESANN.

Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ

Nicolai Petkov, Michael Biehl, Enrique Alegre, 2013, Comput. Methods Programs Biomed..

20th European Symposium on Artificial Neural Networks, ESANN 2012, Bruges, Belgium, April 25-27, 2012

Michael Biehl, Frank-Michael Schleif, Kerstin Bunte, 2012, ESANN.

The Statistical Physics of Learning (in a nutshell): News from the stoneage of neural networks

Michael Biehl, Michael Biehl, 2018 .

Non-Euclidean independent component analysis and Oja's learning

Thomas Villmann, Michael Biehl, Mandy Lange, 2013, ESANN.

Prototype-based analysis of GAMA galaxy catalogue data

Michael Biehl, Lingyu Wang, Aleke Nolte, 2018, ESANN.

Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)

Thomas Villmann, Erzsébet Merényi, Alessandro Sperduti, 2011, Dagstuhl Reports.

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

Thomas Villmann, Michael Biehl, Sven Haase, 2012, Neurocomputing.

Dynamics of Multiple Prototype LVQ

Michael Biehl, Aree Witoelar, Anarta Ghosh, 2006 .

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

Gyan Bhanot, Michael Biehl, Sahand Hormoz, 2015, Bioinform..

Valid interpretation of feature relevance for linear data mappings

Benoît Frénay, Michael Biehl, Barbara Hammer, 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Supervised dimension reduction mappings

Michael Biehl, Barbara Hammer, Kerstin Bunte, 2011, ESANN.

Proc. 21st Europ. Symp. Artificial Neural Networks (ESANN)

Michael Biehl, T. Villmann, Mandy Lange, 2013 .

Learning Vector Quantization: generalization ability and dynamics of competing prototypes

Michael Biehl, Barbara Hammer, Aree Witoelar, 2007, Similarity-based Clustering and its Application to Medicine and Biology.

Machine learning and data analysis in astroinformatics

Peter Tiño, Michael Biehl, Giuseppe Longo, 2018, ESANN.

Statistical mechanics of unsuperised learning

Michael Biehl, Andreas Mietzner, 1993 .

Workshop on New Challenges in Neural Computation 2015

Michael Biehl, Friedrich Melchert, Udo Seiffert, 2015 .

Complex-valued embeddings of generic proximity data pdf

Michael Biehl, Frank-Michael Schleif, Michiel Straat, 2020, S+SSPR.

Data-Driven Supervised Learning for Life Science Data

Michael Biehl, Frank-Michael Schleif, Maximilian Münch, 2020, Frontiers in Applied Mathematics and Statistics.

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

Marcel F. Jonkman, Nicolai Petkov, Michael Biehl, 2015, Expert Syst. Appl..

Adaptive learning for complex-valued data

Michael Biehl, Frank-Michael Schleif, Kerstin Bunte, 2012, ESANN.

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

Thomas Villmann, Michael Biehl, Barbara Hammer, 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

LVQ and SVM Classification of FDG-PET Brain Data

Michael Biehl, Jos B. T. M. Roerdink, Deborah Mudali, 2016, WSOM.

Supervised learning in committee machines by PCA

Michael Biehl, Robert Urbanczik, Christoph Bunzmann, 2002, ESANN.

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

Michael Biehl, Ernest Mwebaze, Michael Biehl, 2016, WSOM.

Proc. 13th Symposium on Artificial Neural Networks (ESANN 2005)

Michael Biehl, Barbara Hammer, Anarta Ghosh, 2005 .

Performance analysis of LVQ algorithms: A statistical physics approach

Michael Biehl, Barbara Hammer, Anarta Ghosh, 2006, Neural Networks.

Phase transitions in vector quantization and neural gas

Michael Biehl, Aree Witoelar, Michael Biehl, 2009, Neurocomputing.

Dimensionality reduction mappings

Michael Biehl, Barbara Hammer, Kerstin Bunte, 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

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

Michael Biehl, Lingyu Wang, Maciej Bilicki, 2019, Neurocomputing.

A General Framework for Dimensionality-Reducing Data Visualization Mapping

Michael Biehl, Barbara Hammer, Kerstin Bunte, 2012, Neural Computation.

Regularization in Matrix Relevance Learning

Thomas Villmann, Michael Biehl, Barbara Hammer, 2010, IEEE Transactions on Neural Networks.

Generalized functional relevance learning vector quantization

Thomas Villmann, Michael Biehl, Barbara Hammer, 2011, ESANN.

Supervised Learning - An Introduction: Lectures given at the 30th Canary Islands Winter School of Astrophysics

Michael Biehl, 2019 .

Handbook of Brain Theory and Neural Networks (second editon)

Michael Biehl, Nestor Caticha, 2002 .

Dimension Reduction and Visualization of Labeled Data

Michael Biehl, Barbara Hammer, Kerstin Bunte, 2009 .

Efficient training of multilayer perceptrons using principal component analysis.

Michael Biehl, Robert Urbanczik, Christoph Bunzmann, 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

Developments in computational intelligence and machine learning

Alessandro Ghio, Michael Biehl, Frank-Michael Schleif, 2015, Neurocomputing.

Segmented shape-symbolic time series representation

Michael Biehl, Ioannis Giotis, Herbert Teun Kruitbosch, 2014, ESANN.

Functional Representation of Prototypes in LVQ and Relevance Learning

Michael Biehl, Friedrich Melchert, Udo Seiffert, 2016, WSOM.

Proc. 6th International Workshop on Self-Organizing Maps

Michael Biehl, Frank-Michael Schleif, Barbara Hammer, 2007 .

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

Michael Biehl, Barbara Hammer, Anarta Ghosh, 2006, Neurocomputing.

17th European Symposium on Artificial Neural Networks (ESANN 2009)

Michael Biehl, Barbara Hammer, Petra Schneider, 2009 .

Towards Emotion Classification Using Appraisal Modeling

Michael Biehl, Paul Lemmens, Dirk Brokken, 2015, Int. J. Synth. Emot..

ROC-Optimization and Statistical Quality Measures in Learning Vector Quantization Classifiers

Michael Biehl, T. Villmann, Marika Kaden, 2014 .

Michael Biehl, Ernest Mwebaze, Dietlind Zühlke, 2015, ESANN.

Adaptive basis functions for prototype-based classification of functional data

Michael Biehl, Friedrich Melchert, Gabriele Bani, 2019, Neural Computing and Applications.

Properties of an adaptive perceptron algorithm

Michael Biehl, R. Eckmiller, G. Hartmann, 1990 .

Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders

Peter Tiño, Michael Biehl, Gert-Jan de Vries, 2017, ESANN.

Feature relevance bounds for ordinal regression pdf

Peter Tiño, Michael Biehl, Barbara Hammer, 2019, ESANN.

Adaptive Matrices for Color Texture Classification

Nicolai Petkov, Michael Biehl, Ioannis Giotis, 2011, CAIP.

Progress in modeling, theory, and application of computational intelligence

Michael Biehl, Cecilio Angulo, Fabrice Rossi, 2008, Neurocomputing.

Facial Expression Recognition Using Learning Vector Quantization

Michael Biehl, Gert-Jan de Vries, Steffen Pauws, 2015, CAIP.

Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data

Axel Wismüller, Michael Biehl, Barbara Hammer, 2010, Neurocomputing.

The AdaTron: An Adaptive Perceptron Algorithm

Michael Biehl, J. K. Anlauf, Michael Biehl, 1989 .

Adaptive basis functions for prototype-based classification of functional data

Michael Biehl, Friedrich Melchert, Udo Seiffert, 2017, Neural Computing and Applications.

Learning paradigms in dynamic environments

Michael Biehl, Barbara Hammer, Kerstin Bunte, 2010 .

Similarity-based learning on structures

Thomas Villmann, Michael Biehl, Barbara Hammer, 2009 .

A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle

George Azzopardi, Michael Biehl, Owen Falzon, 2019, CAIP.

Statistical physics of learning and inference

Thomas Villmann, Michael Biehl, Manfred Opper, 2019, ESANN.

Automatic identification of Holstein cattle using non-invasive computer vision approach

George Azzopardi, Michael Biehl, Amey Bhole, 2018 .

Stationarity of Matrix Relevance Learning Vector Quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2009 .

7th SC@RUG 2010 proceedings: Student Colloquium 2009-2010

Michael Biehl, Rein Smedinga, Femke Kramer, 2010 .

Marker selection for the detection of trisomy 21 using generalized matrix learning vector quantization

Nicolai Petkov, Michael Biehl, Christos Schizas, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261)

Thomas Villmann, Gyan Bhanot, Michael Biehl, 2016, Dagstuhl Reports.

Prototypes and matrix relevance learning in complex fourier space

Thomas Villmann, Michael Biehl, Friedrich Melchert, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

Some steps towards a general principle for dimensionality reduction mappings

Michael Biehl, Barbara Hammer, Kerstin Bunte, 2010, Learning paradigms in dynamic environments.

The dynamics of Learning Vector Quantization

Michael Biehl, Barbara Hammer, Anarta Ghosh, 2005, ESANN.

Texture feature ranking with relevance learning to classify interstitial lung disease patterns

Axel Wismüller, Michael Biehl, Kerstin Bunte, 2012, Artif. Intell. Medicine.

Divergence-based classification in learning vector quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2011, Neurocomputing.

Learning effective color features for content based image retrieval in dermatology

Marcel F. Jonkman, Nicolai Petkov, Michael Biehl, 2011, Pattern Recognit..

The Mathematics of Divergence Based Online Learning in Vector Quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2010, ANNPR.

09081 Abstracts Collection - Similarity-based learning on structures

Thomas Villmann, Michael Biehl, Barbara Hammer, 2009, Similarity-based learning on structures.

Advances in Self-Organizing Maps and Learning Vector Quantization

Michael Biehl, Michael Biehl, 2014 .

Prototype-based classifiers in the presence of concept drift: A modelling framework pdf

Michael Biehl, Barbara Hammer, Christina Göpfert, 2019, WSOM+.

Learning of causal relations

Michael Biehl, Joris M. Mooij, Tom Heskes, 2011, ESANN.

A theoretical framework for analysing the dynamics of LVQ

Michael Biehl, Ansgar Freking, Anarta Ghosh, 2004 .

How to evaluate Dimensionality Reduction (technical report)

Michael Biehl, Bassam Mokbel, Wouter Lueks, 2011 .

Inferring Feature Relevances From Metric Learning

Michael Biehl, Barbara Hammer, Bassam Mokbel, 2015, 2015 IEEE Symposium Series on Computational Intelligence.

Equilibrium Properties of Offline LVQ

Michael Biehl, Barbara Hammer, Aree Witoelar, 2009, ESANN 2009.

Adaptive Metrics for Content Based Image Retrieval in Dermatology

Marcel F. Jonkman, Nicolai Petkov, Michael Biehl, 2009, ESANN.

Generalized matrix learning vector quantizer for the analysis of spectral data

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2008, ESANN.

Distance Learning in Discriminative Vector Quantization

Michael Biehl, Barbara Hammer, Petra Schneider, 2009, Neural Computation.

Dynamics and Generalization Ability of LVQ Algorithms

Michael Biehl, Barbara Hammer, Anarta Ghosh, 2007, J. Mach. Learn. Res..

Complex Variants of GLVQ Based on Wirtinger's Calculus

Thomas Villmann, Michael Biehl, Marika Kaden, 2016, WSOM.

Statistical physics of learning : Phase transitions in multilayered neural networks

Michael Biehl, E. Schlösser, M. Ahr, 2000 .

Supervised learning in the presence of concept drift: a modelling framework pdf

Michael Biehl, Barbara Hammer, Christina Göpfert, 2020, Neural Computing and Applications.

Proc. European Symposium on Artificial Neural Networks

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2007 .

Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution

Fred Keijzer, Michael Biehl, Ronald A. J. van Elburg, 2012, BMC Neuroscience.

A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field

Michael Biehl, Ernest Mwebaze, Godliver Owomugisha, 2020, COMPASS.

Multivariate class labeling in Robust Soft LVQ

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2011, ESANN.

The statistical physics of learning and gen-eralization

Michael Biehl, Nestor Caticha, 2003 .

Some Theoretical Aspects of the Neural Gas Vector Quantizer

Thomas Villmann, Michael Biehl, Barbara Hammer, 2009, Similarity-Based Clustering.

Computational Modelling of Objects Represented in Images - Fundamentals, Methods and Applications III, Third International Symposium, CompIMAGE 2012, Rome, Italy, September 5-7, 2012

Nicolai Petkov, Michael Biehl, Enrique Alegre, 2012, CompIMAGE.

Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2009, IWANN.

Limited Rank Matrix Learning, discriminative dimension reduction and visualization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2012, Neural Networks.

Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection

Thomas Villmann, Michael Biehl, David Nebel, 2015, CAIP.

On-line Learning in Neural Networks

Michael Biehl, Ansgar Freking, E. Schlösser, 1998 .

Artificial Life XII: Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems, Odense, Denmark, August 19-23, 2010

Fred Keijzer, Michael Biehl, O. De Wiljes, 2010, ALIFE.

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

Thomas Villmann, Michel Verleysen, Michael Biehl, 2007, Similarity-based Clustering and its Application to Medicine and Biology.

Analysis of feature relevance using an image quality index applied to digital mammography

Michael Biehl, Andrew D. A. Maidment, Bruno Barufaldi, 2019, Medical Imaging.

Insightful stress detection from physiology modalities using Learning Vector Quantization

Michael Biehl, Gert-Jan de Vries, Steffen Pauws, 2015, Neurocomputing.

Two or three things we know about LVQ

Michael Biehl, 2013 .

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

Thomas Villmann, Axel Wismüller, Michael Biehl, 2010, ESANN.

Window-Based Example Selection in Learning Vector Quantization

Michael Biehl, Barbara Hammer, Gert-Jan de Vries, 2010, Neural Computation.

Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics

Thomas Villmann, Michael Biehl, Barbara Hammer, 2008, ANNPR.

Advances in artificial neural networks, machine learning and computational intelligence

Luca Oneto, Michael Biehl, Fabio Aiolli, 2011, Neurocomputing.

Learning vector quantization classifiers for ROC-optimization

Thomas Villmann, Michael Biehl, Marika Kaden, 2016, Computational Statistics.

Admire LVQ—Adaptive Distance Measures in Relevance Learning Vector Quantization

Michael Biehl, 2012, KI - Künstliche Intelligenz.

Metric Learning for Prototype-Based Classification

Thomas Villmann, Michael Biehl, Barbara Hammer, 2009, Innovations in Neural Information Paradigms and Applications.

About Sparsity in Functional Relevance Learning in Generalized Learning Vector Quantization

Thomas Villmann, Michael Biehl, Marika Kästner, 2011 .

Supervised learning in the presence of concept drift: a modelling framework

Michael Biehl, Barbara Hammer, Christina Göpfert, 2022, Neural Comput. Appl..

Waypoint averaging and step size control in learning by gradient descent (technical report)

Michael Biehl, Kerstin Bunte, G. Papari, 2011 .

Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors

Michael Biehl, Martin Fassnacht, David J. Smith, 2011, The Journal of clinical endocrinology and metabolism.

Statistical physics and practical training of soft-committee machines

Michael Biehl, Robert Urbanczik, Martin Ahr, 1999 .

Dynamical analysis of LVQ type learning rules

Michael Biehl, Barbara Hammer, Anarta Ghosh, 2005 .

Analysis of Flow Cytometry Data by Matrix Relevance Learning Vector Quantization

Michael Biehl, Kerstin Bunte, Petra Schneider, 2013, PloS one.

Off-lattice Kinetic Monte Carlo simulations of Stranski-Krastanov-like growth

Michael Biehl, Michael Biehl, Florian Much, 2003, cond-mat/0307672.

Supervised Learning from Clustered Input Examples

Michael Biehl, Sara A. Solla, Carmela Marangi, 1995 .

Learnability of periodic activation functions: General results

Michael Biehl, Ido Kanter, Michal Rosen-Zvi, 1998 .

Kinetic Monte Carlo simulations of heteroepitaxial growth

Michael Biehl, Wolfgang Kinzel, M. Ahr, 2003 .

Lattice gas models and kinetic Monte Carlo simulations of epitaxial growth

Michael Biehl, 2004 .

Off-lattice Kinetic Monte Carlo Simulations of Strained Heteroepitaxial Growth

Michael Biehl, Christian Vey, Florian Much, 2004 .

A theoretical framework for analyzing the dynamics of learning vector quantization: A statistical physics approach

Michael Biehl, Ansgar Freking, Anarta Ghosh, 2006 .

Matrix Learning in Learning Vector Quantization

Michael Biehl, Barbara Hammer, Petra Schneider, 2006 .

Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data

Michael Biehl, Frank-Michael Schleif, Barbara Hammer, 2007 .

Perceptron learning by constrained optimization: the AdaTron algorithm

Michael Biehl, Wolfgang Kinzel, J. K. Anlauf, 1991 .

A Kinetic Monte Carlo method for the simulation of heteroepitaxial growth

Michael Biehl, Wolfgang Kinzel, M. Ahr, 2002 .

The dynamics of on-line principal component analysis

Michael Biehl, E. Schlösser, Michael Biehl, 1998 .

Specialization processes in on-line unsupervised learning

Michael Biehl, Ansgar Freking, G. Reents, 1998 .

Construction algorithm for the parity-machine

Michael Biehl, Manfred Opper, M. Opper, 1993 .

Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas in the EURINE-ACT study: a prospective test validation study

Michael Biehl, Harm R Haak, Martin Fassnacht, 2020, The lancet. Diabetes & endocrinology.

On{line Learning from Clustered Input Examples On{line Learning from Clustered Input Examples

Michael Biehl, Sara A. Solla, Carmela Marangi, 1995 .

Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism

Michael Biehl, Martin Reincke, Jonathan J. Deeks, 2017, JCI insight.

The somatic genomic landscape of chromophobe renal cell carcinoma.

Lawrence A. Donehower, Peter J. Park, Gyan Bhanot, 2014, Cancer cell.

An Exactly Solvable Model of Unsupervised Learning

Michael Biehl, 1994 .

Matrix adaptation in discriminative vector quantization

Michael Biehl, Barbara Hammer, Petra Schneider, 2008 .