Michael Biehl

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. Artificial Intelligence in Medicine 56(2): 91-97 (2012)

Kerstin Bunte, Sven Haase, Michael Biehl, Thomas Villmann: Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing 90: 23-45 (2012)

Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann: Functional relevance learning in generalized learning vector quantization. Neurocomputing 90: 85-95 (2012)

Michael Biehl: Admire LVQ - Adaptive Distance Measures in Relevance Learning Vector Quantization. KI 26(4): 391-395 (2012)

Kerstin Bunte, Michael Biehl, Barbara Hammer: A General Framework for Dimensionality-Reducing Data Visualization Mapping. Neural Computation 24(3): 771-804 (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: 159-173 (2012)

Marika Kästner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann: Differentiable Kernels in Generalized Matrix Learning Vector Quantization. ICMLA (1) 2012: 132-137

Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann: Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN 2012: 1-8

Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann: Visualization of processes in self-learning systems. PST 2012: 244-249

Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller: Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing 74(9): 1340-1350 (2011)

Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl: Divergence-based classification in learning vector quantization. Neurocomputing 74(9): 1429-1435 (2011)

Kerstin Bunte, Michael Biehl, Marcel F. Jonkman, Nicolai Petkov: Learning effective color features for content based image retrieval in dermatology. Pattern Recognition 44(9): 1892-1902 (2011)

Kerstin Bunte, Ioannis Giotis, Nicolai Petkov, Michael Biehl: Adaptive Matrices for Color Texture Classification. CAIP (2) 2011: 489-497

Kerstin Bunte, Michael Biehl, Barbara Hammer: Dimensionality reduction mappings. CIDM 2011: 349-356

Kerstin Bunte, Michael Biehl, Barbara Hammer: Supervised dimension reduction mappings. ESANN 2011

Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann: Generalized functional relevance learning vector quantization. ESANN 2011

Ernest Mwebaze, John A. Quinn, Michael Biehl: Causal relevance learning for robust classification under interventions. ESANN 2011

John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl: Learning of causal relations. ESANN 2011

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

Barbara Hammer, Michael Biehl, Kerstin Bunte, Bassam Mokbel: A General Framework for Dimensionality Reduction for Large Data Sets. WSOM 2011: 277-287

Wouter Lueks, Bassam Mokbel, Michael Biehl, Barbara Hammer: How to Evaluate Dimensionality Reduction? - Improving the Co-ranking Matrix. CoRR abs/1110.3917 (2011)

Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, Thomas Villmann: Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). Dagstuhl Reports 1(8): 67-95 (2011)

Kerstin Bunte, Barbara Hammer, Axel Wismüller, Michael Biehl: Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data. Neurocomputing 73(7-9): 1074-1092 (2010)

Petra Schneider, Michael Biehl, Barbara Hammer: Hyperparameter learning in probabilistic prototype-based models. Neurocomputing 73(7-9): 1117-1124 (2010)

Aree Witoelar, Anarta Ghosh, J. J. G. de Vries, Barbara Hammer, Michael Biehl: Window-Based Example Selection in Learning Vector Quantization. Neural Computation 22(11): 2924-2961 (2010)

Petra Schneider, Kerstin Bunte, Han Stiekema, Barbara Hammer, Thomas Villmann, Michael Biehl: Regularization in matrix relevance learning. IEEE Transactions on Neural Networks 21(5): 831-840 (2010)

Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl: The Mathematics of Divergence Based Online Learning in Vector Quantization. ANNPR 2010: 108-119

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

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

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl: Generalized Derivative Based Kernelized Learning Vector Quantization. IDEAL 2010: 21-28

Frank-Michael Schleif, Michael Biehl, Alfredo Vellido: Advances in machine learning and computational intelligence. Neurocomputing 72(7-9): 1377-1378 (2009)

Aree Witoelar, Michael Biehl: Phase transitions in vector quantization and neural gas. Neurocomputing 72(7-9): 1390-1397 (2009)

Petra Schneider, Michael Biehl, Barbara Hammer: Distance Learning in Discriminative Vector Quantization. Neural Computation 21(10): 2942-2969 (2009)

Petra Schneider, Michael Biehl, Barbara Hammer: Adaptive Relevance Matrices in Learning Vector Quantization. Neural Computation 21(12): 3532-3561 (2009)

Kerstin Bunte, Barbara Hammer, Michael Biehl: Nonlinear Dimension Reduction and Visualization of Labeled Data. CAIP 2009: 1162-1170

Michael Biehl, Nestor Caticha, Peter Riegler: Statistical Mechanics of On-line Learning. Similarity-Based Clustering 2009: 1-22

Thomas Villmann, Barbara Hammer, Michael Biehl: Some Theoretical Aspects of the Neural Gas Vector Quantizer. Similarity-Based Clustering 2009: 23-34

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

Kerstin Bunte, Barbara Hammer, Petra Schneider, Michael Biehl: Nonlinear Discriminative Data Visualization. ESANN 2009

Petra Schneider, Michael Biehl, Barbara Hammer: Hyperparameter Learning in Robust Soft LVQ. ESANN 2009

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

Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl: Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. IWANN (1) 2009: 933-940

Michael Biehl, Barbara Hammer, Petra Schneider, Thomas Villmann: Metric Learning for Prototype-Based Classification. Innovations in Neural Information Paradigms and Applications 2009: 183-199

Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann (Eds.): Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar]. Lecture Notes in Computer Science 5400, Springer 2009, isbn 978-3-642-01804-6

Enrique Alegre, Michael Biehl, Nicolai Petkov, Lidia Sánchez: Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. Comp. in Bio. and Med. 38(4): 461-468 (2008)

Fabrice Rossi, Michael Biehl, Cecilio Angulo Bahón: Progress in modeling, theory, and application of computational intelligence. Neurocomputing 71(7-9): 1117-1119 (2008)

Aree Witoelar, Michael Biehl, Anarta Ghosh, Barbara Hammer: Learning dynamics and robustness of vector quantization and neural gas. Neurocomputing 71(7-9): 1210-1219 (2008)

Marc Strickert, Petra Schneider, Jens Keilwagen, Thomas Villmann, Michael Biehl, Barbara Hammer: Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. ANNPR 2008: 78-89

Aree Witoelar, Anarta Ghosh, Michael Biehl: Phase transitions in Vector Quantization. ESANN 2008: 221-226

Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl: Generalized matrix learning vector quantizer for the analysis of spectral data. ESANN 2008: 451-456

Michael Biehl, Erzsébet Merényi, Fabrice Rossi: Advances in computational intelligence and learning. Neurocomputing 70(7-9): 1117-1119 (2007)

Michael Biehl, Anarta Ghosh, Barbara Hammer: Dynamics and Generalization Ability of LVQ Algorithms. Journal of Machine Learning Research 8: 323-360 (2007)

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

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

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

Petra Schneider, Michael Biehl, Barbara Hammer: Relevance matrices in LVQ. ESANN 2007: 37-42

Aree Witoelar, Michael Biehl, Anarta Ghosh, Barbara Hammer: On the dynamics of Vector Quantization and Neural Gas. ESANN 2007: 127-132

Michael Biehl, Rainer Breitling, Yang Li: Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning. IDEAL 2007: 880-889

Michael Biehl, Barbara Hammer, Michel Verleysen, Thomas Villmann (Eds.): Similarity-based Clustering and its Application to Medicine and Biology, 25.03. - 30.03.2007. Dagstuhl Seminar Proceedings 07131, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2007

Michael Biehl, Anarta Ghosh, Barbara Hammer: Learning vector quantization: The dynamics of winner-takes-all algorithms. Neurocomputing 69(7-9): 660-670 (2006)

Anarta Ghosh, Michael Biehl, Barbara Hammer: Performance analysis of LVQ algorithms: A statistical physics approach. Neural Networks 19(6-7): 817-829 (2006)

Michael Biehl, Piter Pasma, Marten Pijl, Lidia Sánchez, Nicolai Petkov: Classification of Boar Sperm Head Images using Learning Vector Quantization. ESANN 2006: 545-550

Michael Biehl, Anarta Ghosh, Barbara Hammer: The dynamics of Learning Vector Quantization. ESANN 2005: 13-18

Christoph Bunzmann, Michael Biehl, Robert Urbanczik: Supervised learning in committee machines by PCA. ESANN 2002: 125-130