☰

cached image

Michael Biehl - Publications

Affiliations: 
1988-1992 Physics Unviversity of Gießen, Germany 
 1992-2003 Theoretical Physics University of Würzburg, Würzburg, Bayern, Germany 
 2003- Johann Bernoulli Institute for Mathematics and Computer Science University of Groningen, Groningen, Netherlands 
Area:
Statistical Physics, Machine Learning
Website:
http://www.cs.rug.nl/~biehl/
Tree Info Similar researchers PubMed Report error

124 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2024 van Veen R, Tamboli NRB, Lövdal S, Meles SK, Renken RJ, de Vries GJ, Arnaldi D, Morbelli S, Clavero P, Obeso JA, Oroz MCR, Leenders KL, Villmann T, Biehl M. Subspace corrected relevance learning with application in neuroimaging. Artificial Intelligence in Medicine. 149: 102786. PMID 38462286 DOI: 10.1016/j.artmed.2024.102786  0.64
2023 Htun HH, Biehl M, Petkov N. Survey of feature selection and extraction techniques for stock market prediction. Financial Innovation. 9: 26. PMID 36687795 DOI: 10.1186/s40854-022-00441-7  0.578
2020 van Veen R, Gurvits V, Kogan RV, Meles SK, de Vries GJ, Renken RJ, Rodriguez-Oroz MC, Rodriguez-Rojas R, Arnaldi D, Raffa S, de Jong BM, Leenders KL, Biehl M. An application of generalized matrix learning vector quantization in neuroimaging. Computer Methods and Programs in Biomedicine. 197: 105708. PMID 32977181 DOI: 10.1016/J.Cmpb.2020.105708  0.356
2020 Owomugisha G, Mugagga PKB, Melchert F, Mwebaze E, Quinn JA, Biehl M. A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field. The Compass. 331-332. DOI: 10.1145/3378393.3402252  0.727
2020 Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information Neurocomputing. DOI: 10.1016/J.Neucom.2019.12.133  0.357
2019 Nolte A, Wang L, Bilicki M, Holwerda B, Biehl M. Galaxy classification: A machine learning analysis of GAMA catalogue data Neurocomputing. 342: 172-190. DOI: 10.1016/J.Neucom.2018.12.076  0.371
2019 Melchert F, Bani G, Seiffert U, Biehl M. Adaptive basis functions for prototype-based classification of functional data Neural Computing and Applications. 1-11. DOI: 10.1007/S00521-019-04299-2  0.362
2019 Straat M, Kaden M, Villmann T, Lampe A, Seiffert U, Biehl M, Melchert F. Learning vector quantization and relevances in complex coefficient space Neural Computing and Applications. 1-15. DOI: 10.1007/S00521-019-04080-5  0.396
2018 Straat M, Abadi F, Göpfert C, Hammer B, Biehl M. Statistical Mechanics of On-Line Learning Under Concept Drift. Entropy (Basel, Switzerland). 20. PMID 33265863 DOI: 10.3390/e20100775  0.357
2018 Straat M, Abadi F, Göpfert C, Hammer B, Biehl M. Statistical Mechanics of On-Line Learning Under Concept Drift Entropy. 20: 775. DOI: 10.3390/E20100775  0.463
2018 Aiolli F, Biehl M, Oneto L. Advances in artificial neural networks, machine learning and computational intelligence Neurocomputing. 298: 1-3. DOI: 10.1016/J.Neucom.2017.04.038  0.356
2016 Biehl M, Hammer B, Villmann T. Prototype-based models in machine learning. Wiley Interdisciplinary Reviews. Cognitive Science. 7: 92-111. PMID 26800334 DOI: 10.1002/Wcs.1378  0.668
2016 Mudali D, Biehl M, Meles SK, Renken RJ, García-García D, Clavero P, Arbizu J, Obeso JA, Rodriguez-Oroz MC, Leenders KL, Roerdink JBTM. Differentiating Early and Late Stage Parkinson’s Disease Patients from Healthy Controls Journal of Biomedical Engineering and Medical Imaging. 3: 33-43. DOI: 10.14738/Jbemi.36.2280  0.769
2016 Biehl M, Hammer B, Villmann T. Prototype-based Models for the Supervised Learning of Classification Schemes Proceedings of the International Astronomical Union. 12: 129-138. DOI: 10.1017/S1743921316012928  0.639
2016 Villmann T, Kaden M, Hermann W, Biehl M. Learning vector quantization classifiers for ROC-optimization Computational Statistics. 1-22. DOI: 10.1007/S00180-016-0678-Y  0.67
2016 Mwebaze E, Biehl M. Prototype-based classification for image analysis and its application to crop disease diagnosis Advances in Intelligent Systems and Computing. 428: 329-339. DOI: 10.1007/978-3-319-28518-4_29  0.731
2016 Gay M, Kaden M, Biehl M, Lampe A, Villmann T. Complex variants of GLVQ based on Wirtinger’s calculus Advances in Intelligent Systems and Computing. 428: 293-303. DOI: 10.1007/978-3-319-28518-4_26  0.48
2016 Biehl M, Hammer B, Villmann T. Prototype-based models in machine learning Wiley Interdisciplinary Reviews: Cognitive Science. 7: 92-111. DOI: 10.1002/wcs.1378  0.599
2015 Biehl M, Hammer B, Schleif FM, Schneider P, Villmann T. Stationarity of Matrix Relevance LVQ Proceedings of the International Joint Conference On Neural Networks. 2015. DOI: 10.1109/IJCNN.2015.7280441  0.502
2015 Biehl M, Ghio A, Schleif FM. Developments in computational intelligence and machine learning Neurocomputing. DOI: 10.1016/J.Neucom.2015.03.062  0.362
2015 de Vries GJJ, Pauws SC, Biehl M. Insightful stress detection from physiology modalities using Learning Vector Quantization Neurocomputing. 151: 873-882. DOI: 10.1016/J.Neucom.2014.10.008  0.349
2015 Lange M, Biehl M, Villmann T. Non-Euclidean principal component analysis by Hebbian learning Neurocomputing. 147: 107-119. DOI: 10.1016/J.Neucom.2013.11.049  0.642
2015 Giotis I, Molders N, Land S, Biehl M, Jonkman MF, Petkov N. MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images Expert Systems With Applications. 42: 6578-6585. DOI: 10.1016/J.Eswa.2015.04.034  0.604
2015 Schleif FM, Hammer B, Monroy JG, Jimenez JG, Blanco-Claraco JL, Biehl M, Petkov N. Odor recognition in robotics applications by discriminative time-series modeling Pattern Analysis and Applications. DOI: 10.1007/S10044-014-0442-2  0.649
2015 Villmann T, Kaden M, Nebel D, Biehl M. Learning vector quantization with adaptive cost-based outlier-rejection Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9257: 772-782. DOI: 10.1007/978-3-319-23117-4_66  0.6
2014 Biehl M, Hammer B, Villmann T. Distance measures for prototype based classification Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8603: 100-116. DOI: 10.1007/978-3-319-12084-3_9  0.521
2013 Alegre E, Biehl M, Petkov N, Sanchez L. Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ Computer Methods and Programs in Biomedicine. 111: 525-536. PMID 23790406 DOI: 10.1016/J.Cmpb.2013.05.003  0.64
2013 Biehl M, Bunte K, Schneider P. Analysis of Flow Cytometry Data by Matrix Relevance Learning Vector Quantization Plos One. 8. PMID 23527184 DOI: 10.1371/Journal.Pone.0059401  0.809
2013 Strickert M, Hammer B, Villmann T, Biehl M. Regularization and improved interpretation of linear data mappings and adaptive distance measures Proceedings of the 2013 Ieee Symposium On Computational Intelligence and Data Mining, Cidm 2013 - 2013 Ieee Symposium Series On Computational Intelligence, Ssci 2013. 10-17. DOI: 10.1109/CIDM.2013.6597211  0.522
2013 Giotis I, Bunte K, Petkov N, Biehl M. Erratum to: Adaptive Matrices and Filters for Color Texture Classification Journal of Mathematical Imaging and Vision. 48: 202-202. DOI: 10.1007/S10851-013-0472-1  0.776
2013 Giotis I, Bunte K, Petkov N, Biehl M. Adaptive matrices and filters for color texture classification Journal of Mathematical Imaging and Vision. 47: 79-92. DOI: 10.1007/S10851-012-0356-9  0.803
2013 Biehl M, Kästner M, Lange M, Villmann T. Non-Euclidean principal component analysis and Oja's learning rule - Theoretical aspects Advances in Intelligent Systems and Computing. 198: 23-33. DOI: 10.1007/978-3-642-35230-0_3  0.648
2012 Huber MB, Bunte K, Nagarajan MB, Biehl M, Ray LA, Wismüller A. Texture feature ranking with relevance learning to classify interstitial lung disease patterns. Artificial Intelligence in Medicine. 56: 91-7. PMID 23010586 DOI: 10.1016/J.Artmed.2012.07.001  0.744
2012 Bunte K, Schneider P, Hammer B, Schleif FM, Villmann T, Biehl M. Limited Rank Matrix Learning, discriminative dimension reduction and visualization Neural Networks. 26: 159-173. PMID 22041220 DOI: 10.1016/J.Neunet.2011.10.001  0.809
2012 Bunte K, Schneider P, Hammer B, Schleif FM, Villmann T, Biehl M. Limited Rank Matrix Learning, discriminative dimension reduction and visualization Neural Networks. 26: 159-173. PMID 22041220 DOI: 10.1016/j.neunet.2011.10.001  0.305
2012 Bunte K, Biehl M, Hammer B. A general framework for dimensionality-reducing data visualization mapping Neural Computation. 24: 771-804. DOI: 10.1162/Neco_A_00250  0.716
2012 Peters G, Bunte K, Strickert M, Biehl M, Villmann T. Visualization of processes in self-learning systems 2012 10th Annual International Conference On Privacy, Security and Trust, Pst 2012. 244-249. DOI: 10.1109/PST.2012.6297953  0.771
2012 Biehl M, Bunte K, Schleif FM, Schneider P, Villmann T. Large margin linear discriminative visualization by matrix relevance learning Proceedings of the International Joint Conference On Neural Networks. DOI: 10.1109/IJCNN.2012.6252627  0.785
2012 Kastner M, Nebel D, Riedel M, Biehl M, Villmann T. Differentiable kernels in generalized matrix learning vector quantization Proceedings - 2012 11th International Conference On Machine Learning and Applications, Icmla 2012. 1: 132-137. DOI: 10.1109/ICMLA.2012.231  0.614
2012 Bunte K, Haase S, Biehl M, Villmann T. Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences Neurocomputing. 90: 23-45. DOI: 10.1016/J.Neucom.2012.02.034  0.771
2012 Kästner M, Hammer B, Biehl M, Villmann T. Functional relevance learning in generalized learning vector quantization Neurocomputing. 90: 85-95. DOI: 10.1016/J.Neucom.2011.11.029  0.669
2012 Biehl M. Admire LVQ—Adaptive Distance Measures in Relevance Learning Vector Quantization Ki - KüNstliche Intelligenz. 26: 391-395. DOI: 10.1007/S13218-012-0188-1  0.421
2011 Arlt W, Biehl M, Taylor AE, Hahner S, Libé R, Hughes BA, Schneider P, Smith DJ, Stiekema H, Krone N, Porfiri E, Opocher G, Bertherat J, Mantero F, Allolio B, et al. Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors. The Journal of Clinical Endocrinology and Metabolism. 96: 3775-84. PMID 21917861 DOI: 10.1210/Jc.2011-1565  0.537
2011 Huber MB, Bunte K, Nagarajan MB, Biehl M, Ray LA, Wismueller A. Texture feature selection with relevance learning to classify interstitial lung disease patterns Progress in Biomedical Optics and Imaging - Proceedings of Spie. 7963. DOI: 10.1117/12.877894  0.75
2011 Bunte K, Biehl M, Hammer B. Dimensionality reduction mappings Ieee Ssci 2011: Symposium Series On Computational Intelligence - Cidm 2011: 2011 Ieee Symposium On Computational Intelligence and Data Mining. 349-356. DOI: 10.1109/CIDM.2011.5949443  0.659
2011 Bunte K, Biehl M, Jonkman MF, Petkov N. Learning effective color features for content based image retrieval in dermatology Pattern Recognition. 44: 1892-1902. DOI: 10.1016/J.Patcog.2010.10.024  0.793
2011 Bunte K, Hammer B, Villmann T, Biehl M, Wismüller A. Neighbor embedding XOM for dimension reduction and visualization Neurocomputing. 74: 1340-1350. DOI: 10.1016/J.Neucom.2010.11.027  0.796
2011 Mwebaze E, Schneider P, Schleif FM, Aduwo JR, Quinn JA, Haase S, Villmann T, Biehl M. Divergence-based classification in learning vector quantization Neurocomputing. 74: 1429-1435. DOI: 10.1016/J.Neucom.2010.10.016  0.796
2011 Bunte K, Giotis I, Petkov N, Biehl M. Adaptive matrices for color texture classification Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6855: 489-497. DOI: 10.1007/978-3-642-23678-5_58  0.766
2011 Hammer B, Biehl M, Bunte K, Mokbel B. A general framework for dimensionality reduction for large data sets Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6731: 277-287. DOI: 10.1007/978-3-642-21566-7_28  0.692
2010 Witoelar AW, Ghosh A, de Vries JJ, Hammer B, Biehl M. Window-based example selection in learning vector quantization. Neural Computation. 22: 2924-61. PMID 20804387 DOI: 10.1162/Neco_A_00030  0.822
2010 Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M. Regularization in matrix relevance learning. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 21: 831-40. PMID 20236882 DOI: 10.1109/Tnn.2010.2042729  0.825
2010 Schneider P, Biehl M, Hammer B. Hyperparameter learning in probabilistic prototype-based models Neurocomputing. 73: 1117-1124. DOI: 10.1016/J.Neucom.2009.11.021  0.668
2010 Bunte K, Hammer B, Wismüller A, Biehl M. Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data Neurocomputing. 73: 1074-1092. DOI: 10.1016/J.Neucom.2009.11.017  0.755
2010 Schleif FM, Villmann T, Hammer B, Schneider P, Biehl M. Generalized derivative based kernelized learning vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6283: 21-28. DOI: 10.1007/978-3-642-15381-5_3  0.625
2010 Villmann T, Haase S, Schleif FM, Hammer B, Biehl M. The mathematics of divergence based online learning in vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5998: 108-119. DOI: 10.1007/978-3-642-12159-3_10  0.811
2010 Mwebaze E, Schneider P, Schleif FM, Haase S, Villmann T, Biehl M. Divergence based learning vector quantization Proceedings of the 18th European Symposium On Artificial Neural Networks - Computational Intelligence and Machine Learning, Esann 2010. 247-252.  0.303
2009 Schneider P, Biehl M, Hammer B. Adaptive relevance matrices in learning vector quantization. Neural Computation. 21: 3532-61. PMID 19764875 DOI: 10.1162/Neco.2009.11-08-908  0.68
2009 Schneider P, Biehl M, Hammer B. Distance learning in discriminative vector quantization. Neural Computation. 21: 2942-69. PMID 19635012 DOI: 10.1162/Neco.2009.10-08-892  0.686
2009 Schleif FM, Biehl M, Vellido A. Advances in machine learning and computational intelligence Neurocomputing. 72: 1377-1378. DOI: 10.1016/J.Neucom.2008.12.013  0.361
2009 Witoelar A, Biehl M. Phase transitions in vector quantization and neural gas Neurocomputing. 72: 1390-1397. DOI: 10.1016/J.Neucom.2008.10.023  0.424
2009 Biehl M, Hammer B, Schneider P, Villmann T. Metric learning for prototype-based classification Studies in Computational Intelligence. 247: 183-199. DOI: 10.1007/978-3-642-04003-0_8  0.767
2009 Bunte K, Hammer B, Biehl M. Nonlinear dimension reduction and visualization of labeled data Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5702: 1162-1170. DOI: 10.1007/978-3-642-03767-2_141  0.688
2009 Strickert M, Keilwagen J, Schleif FM, Villmann T, Biehl M. Matrix metric adaptation linear discriminant analysis of biomedical data Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5517: 933-940. DOI: 10.1007/978-3-642-02478-8_117  0.588
2009 Villmann T, Hammer B, Biehl M. Some theoretical aspects of the neural gas vector quantizer Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5400: 23-34. DOI: 10.1007/978-3-642-01805-3_2  0.586
2009 Biehl M, Caticha N, Riegler P. Statistical mechanics of on-line learning Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5400: 1-22. DOI: 10.1007/978-3-642-01805-3_1  0.821
2009 Biehl M, Hammer B, Verleysen M, Villmann T. Similarity-Based Clustering: Recent Developments and Biomedical Applications Springer Us. 5400. DOI: 10.1007/978-3-642-01805-3  0.631
2008 Weber S, Biehl M, Kotrla M, Kinzel W. Simulation of self-assembled nanopatterns in strained 2D alloys on the face centered cubic (111) surface. Journal of Physics. Condensed Matter : An Institute of Physics Journal. 20: 265004. PMID 21694353 DOI: 10.1088/0953-8984/20/26/265004  0.674
2008 Alegre E, Biehl M, Petkov N, Sánchez L. Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. Computers in Biology and Medicine. 38: 461-8. PMID 18339365 DOI: 10.1016/J.Compbiomed.2008.01.005  0.635
2008 Witoelar A, Biehl M, Ghosh A, Hammer B. Learning dynamics and robustness of vector quantization and neural gas Neurocomputing. 71: 1210-1219. DOI: 10.1016/J.Neucom.2007.11.022  0.816
2008 Strickert M, Schneider P, Keilwagen J, Villmann T, Biehl M, Hammer B. Discriminatory data mapping by matrix-based supervised learning metrics Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5064: 78-89. DOI: 10.1007/978-3-540-69939-2_8  0.596
2007 Biehl M, Merényi E, Rossi F. Advances in computational intelligence and learning Neurocomputing. 70: 1117-1119. DOI: 10.1016/J.Neucom.2006.12.001  0.368
2007 Walther M, Biehl M, Kinzel W. Formation and consequences of misfit dislocations in heteroepitaxial growth Physica Status Solidi (C) Current Topics in Solid State Physics. 4: 3210-3220. DOI: 10.1002/Pssc.200775414  0.771
2006 Ghosh A, Biehl M, Hammer B. Performance analysis of LVQ algorithms: a statistical physics approach. Neural Networks : the Official Journal of the International Neural Network Society. 19: 817-29. PMID 16781845 DOI: 10.1016/j.neunet.2006.05.010  0.806
2006 Biehl M, Ghosh A, Hammer B. Learning vector quantization: The dynamics of winner-takes-all algorithms Neurocomputing. 69: 660-670. DOI: 10.1016/J.Neucom.2005.12.007  0.825
2005 Bunzmann C, Biehl M, Urbanczik R. Efficient training of multilayer perceptrons using principal component analysis. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 72: 026117. PMID 16196654 DOI: 10.1103/Physreve.72.026117  0.787
2004 Volkmann T, Ahr M, Biehl M. Kinetic model of II-VI(001) semiconductor surfaces: Growth rates in atomic layer epitaxy Physical Review B - Condensed Matter and Materials Physics. 69: 165303-1-165303-10. DOI: 10.1103/Physrevb.69.165303  0.768
2003 Biehl M, Ahr M, Kinzel W, Much F. Kinetic Monte Carlo simulations of heteroepitaxial growth Thin Solid Films. 428: 52-55. DOI: 10.1016/S0040-6090(02)01267-1  0.772
2003 Biehl M. The Statistical Physics of Learning: Phase Transitions and Dynamical Symmetry Breaking Springer Us. 89-101. DOI: 10.1007/978-3-662-05594-6_9  0.313
2002 Biehl M, Kinzel W. Terrace Sizes and Particle Currents in Epitaxial Growth Jsme International Journal Series B-Fluids and Thermal Engineering. 45: 112-116. DOI: 10.1299/Jsmeb.45.112  0.682
2002 Ahr M, Biehl M. Flat (001) surfaces of II–VI semiconductors: a lattice gas model Surface Science. 505: 124-136. DOI: 10.1016/S0039-6028(02)01145-7  0.765
2002 Much F, Ahr M, Biehl M, Kinzel W. A kinetic Monte Carlo method for the simulation of heteroepitaxial growth Computer Physics Communications. 147: 226-229. DOI: 10.1016/S0010-4655(02)00251-5  0.768
2002 Ahr M, Biehl M, Volkmann T. Modeling (001) surfaces of II–VI semiconductors Computer Physics Communications. 147: 107-110. DOI: 10.1016/S0010-4655(02)00226-6  0.774
2001 Bunzmann C, Biehl M, Urbanczik R. Efficiently learning multilayer perceptrons. Physical Review Letters. 86: 2166-9. PMID 11289881 DOI: 10.1103/Physrevlett.86.2166  0.798
2001 Ahr FMM, Biehl M, Kinzel W. Kinetic Monte Carlo simulations of dislocations in heteroepitaxial growth Epl. 56: 791-796. DOI: 10.1209/Epl/I2001-00589-8  0.688
2001 Biehl M, Ahr M, Kinzel W, Sokolowski M, Volkmann T. A lattice gas model of II-VI(001) semiconductor surfaces Europhysics Letters (Epl). 53: 169-175. DOI: 10.1209/Epl/I2001-00132-1  0.77
2001 Biehl M, Ahr M, Kinne M, Kinzel W, Schinzer S. Particle currents and the distribution of terrace sizes in unstable epitaxial growth Physical Review B. 64. DOI: 10.1103/Physrevb.64.113405  0.765
2001 Biehl M, Kühn R, Stamatescu IO. CORRIGENDUM: Learning structured data from unspecific reinforcement Journal of Physics A. 34: 4267. DOI: 10.1088/0305-4470/34/19/501  0.373
2001 Biehl M, Bunzmann C, Urbanczik R. Training multilayer perceptrons by principal component analysis Physica a: Statistical Mechanics and Its Applications. 302: 56-63. DOI: 10.1016/S0378-4371(01)00440-X  0.778
2001 Ahr M, Biehl M. Modelling sublimation and atomic layer epitaxy in the presence of competing surface reconstructions Surface Science. 488: L553-L560. DOI: 10.1016/S0039-6028(01)01157-8  0.765
2000 Ahr M, Biehl M. Singularity spectra of rough growing surfaces from wavelet analysis Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 62: 1773-7. PMID 11088639 DOI: 10.1103/Physreve.62.1773  0.767
2000 Biehl M, Kühn R, Stamatescu I. Learning structured data from unspecific reinforcement Journal of Physics a: Mathematical and General. 33: 6843-6857. DOI: 10.1088/0305-4470/33/39/302  0.39
2000 Ahr M, Biehl M, Kinne M, Kinzel W. The influence of the crystal lattice on coarsening in unstable epitaxial growth Surface Science. 465: 339-346. DOI: 10.1016/S0039-6028(00)00725-1  0.765
1999 Freking A, Biehl M, Braun C, Kinzel W, Meesmann M. Receiver operating characteristics of perceptrons: influence of sample size and prevalence. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 60: 5926-31. PMID 11970494 DOI: 10.1103/Physreve.60.5926  0.694
1999 Schinzer S, Sokolowski M, Biehl M, Kinzel W. Unconventional MBE strategies from computer simulations for optimized growth conditions Physical Review B. 60: 2893-2899. DOI: 10.1103/Physrevb.60.2893  0.681
1999 Ahr M, Biehl M, Urbanczik R. Noisy regression and classification with continuous multilayer networks Journal of Physics a: Mathematical and General. 32: L531-L536. DOI: 10.1088/0305-4470/32/50/101  0.788
1999 Ahr M, Biehl M, Schlösser E. Weight-decay induced phase transitions in multilayer neural networks Journal of Physics a: Mathematical and General. 32: 5003-5008. DOI: 10.1088/0305-4470/32/27/301  0.771
1999 Schlösser E, Saad D, Biehl M. Optimization of on-line principal component analysis Journal of Physics A. 32: 4061-4067. DOI: 10.1088/0305-4470/32/22/306  0.388
1999 Schinzer S, Kinne M, Biehl M, Kinzel W. The role of step edge diffusion in epitaxial crystal growth Surface Science. 439: 191-198. DOI: 10.1016/S0039-6028(99)00761-X  0.687
1999 Schinzer S, Sokolowski M, Biehl M, Kinzel W. Evaporation and step edge diffusion in MBE Journal of Crystal Growth. 201202: 85-87. DOI: 10.1016/S0022-0248(98)01293-7  0.681
1999 Biehl M, Kinne M, Kinzel W, Schinzer S. A simple model of epitaxial growth: the influence of step edge diffusion Computer Physics Communications. 347-352. DOI: 10.1016/S0010-4655(99)00351-3  0.684
1999 Ahr M, Biehl M, Urbanczik R. Statistical physics and practical training of soft-committee machines The European Physical Journal B. 10: 583-588. DOI: 10.1007/S100510050889  0.805
1998 Biehl M, Schlösser E, Ahr M. Phase transitions in soft-committee machines Europhysics Letters (Epl). 44: 261-267. DOI: 10.1209/Epl/I1998-00466-6  0.78
1998 Biehl M, Kinzel W, Schinzer S. A simple model of epitaxial growth Epl. 41: 443-448. DOI: 10.1209/Epl/I1998-00171-0  0.692
1998 Rosen-Zvi M, Biehl M, Kanter I. Learnability of periodic activation functions: General results Physical Review E. 58: 3606-3609. DOI: 10.1103/Physreve.58.3606  0.365
1998 Biehl M, Schlösser E. The dynamics of on-line principal component analysis Journal of Physics a: Mathematical and General. 31: L97-L103. DOI: 10.1088/0305-4470/31/5/002  0.405
1998 Biehl M, Freking A, Reents G, Schlösser E. Specialization processes in on-line unsupervised learning Philosophical Magazine B. 77: 1487-1494. DOI: 10.1080/13642819808205040  0.452
1997 Biehl M, Freking A, Reents G. Dynamics of on-line competitive learning Europhysics Letters (Epl). 38: 73-78. DOI: 10.1209/Epl/I1997-00536-9  0.418
1997 Copelli M, Eichhorn R, Kinouchi O, Biehl M, Simonetti R, Riegler P, Caticha N. Noise robustness in multilayer neural networks Europhysics Letters. 37: 427-432. DOI: 10.1209/Epl/I1997-00167-2  0.783
1997 Biehl M, Riegler P. Comment on ``On-Line Gibbs Learning'' Physical Review Letters. 78: 4305-4305. DOI: 10.1103/Physrevlett.78.4305  0.735
1996 Biehl M, Riegler P, Wöhler C. Transient dynamics of on-line learning in two-layered neural networks Journal of Physics a: Mathematical and General. 29: 4769-4780. DOI: 10.1088/0305-4470/29/16/005  0.749
1995 Biehl M, Riegler P, Stechert M. Learning from noisy data: An exactly solvable model. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 52: R4624-R4627. PMID 9964090 DOI: 10.1103/Physreve.52.R4624  0.748
1995 Marangi C, Biehl M, Solla SA. Supervised Learning from Clustered Input Examples Europhysics Letters (Epl). 30: 117-122. DOI: 10.1209/0295-5075/30/2/010  0.341
1995 Biehl M, Schwarze H. Learning by on-line gradient descent Journal of Physics a: Mathematical and General. 28: 643-656. DOI: 10.1088/0305-4470/28/3/018  0.445
1995 Riegler P, Biehl M. On-line backpropagation in two-layered neural networks Journal of Physics a: Mathematical and General. 28: L507-L513. DOI: 10.1088/0305-4470/28/20/002  0.717
1994 Biehl M, Riegler P. On-Line Learning with a Perceptron Europhysics Letters (Epl). 28: 525-530. DOI: 10.1209/0295-5075/28/7/012  0.744
1994 Biehl M. An Exactly Solvable Model of Unsupervised Learning Europhysics Letters (Epl). 25: 391-396. DOI: 10.1209/0295-5075/25/5/014  0.443
1994 Biehl M, Mietzner A. Statistical mechanics of unsupervised structure recognition Journal of Physics a: Mathematical and General. 27: 1885-1897. DOI: 10.1088/0305-4470/27/6/015  0.435
1993 Biehl M, Mietzner A. Statistical Mechanics of Unsupervised Learning Europhysics Letters (Epl). 24: 421-426. DOI: 10.1209/0295-5075/24/5/017  0.44
1993 Watkin TLH, Rau A, Biehl M. The Statistical-Mechanics Of Learning A Rule Reviews of Modern Physics. 65: 499-556. DOI: 10.1103/Revmodphys.65.499  0.376
1993 Biehl M, Schwarze H. Learning drifting concepts with neural networks Journal of Physics a: Mathematical and General. 26: 2651-2665. DOI: 10.1088/0305-4470/26/11/014  0.423
1992 Biehl M, Schwarze H. On-Line Learning of a Time-Dependent Rule Europhysics Letters (Epl). 20: 733-738. DOI: 10.1209/0295-5075/20/8/012  0.413
1991 Biehl M, Opper M. Tilinglike learning in the parity machine. Physical Review. A. 44: 6888-6894. PMID 9905815 DOI: 10.1103/Physreva.44.6888  0.343
1989 Anlauf JK, Biehl M. The AdaTron: An Adaptive Perceptron Algorithm Europhysics Letters (Epl). 10: 687-692. DOI: 10.1209/0295-5075/10/7/014  0.384
Show low-probability matches.