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/

96 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
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  0.48
2020 Bancos I, Taylor AE, Chortis V, Sitch AJ, Jenkinson C, Davidge-Pitts CJ, Lang K, Tsagarakis S, Macech M, Riester A, Deutschbein T, Pupovac ID, Kienitz T, Prete A, Papathomas TG, ... ... Biehl M, et al. Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas in the EURINE-ACT study: a prospective test validation study. The Lancet. Diabetes & Endocrinology. PMID 32711725 DOI: 10.1016/S2213-8587(20)30218-7  1
2020 Panda A, Yadav A, Yeerna H, Singh A, Biehl M, Lux M, Schulz A, Klecha T, Doniach S, Khiabanian H, Ganesan S, Tamayo P, Bhanot G. Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples. Nucleic Acids Research. PMID 32525984 DOI: 10.1093/nar/gkaa485  1
2019 Chortis V, Bancos I, Nijman T, Gilligan LC, Taylor AE, Ronchi CL, O'Reilly MW, Schreiner J, Asia M, Riester A, Perotti P, Libé R, Quinkler M, Canu L, Paiva I, ... ... Biehl M, et al. Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma. The Journal of Clinical Endocrinology and Metabolism. PMID 31665449 DOI: 10.1210/clinem/dgz141  1
2018 Idema DL, Wang Y, Biehl M, Horvatovich PL, Hak E. Effect estimate comparison between the prescription sequence symmetry analysis (PSSA) and parallel group study designs: A systematic review. Plos One. 13: e0208389. PMID 30521568 DOI: 10.1371/journal.pone.0208389  0.04
2017 Arlt W, Lang K, Sitch AJ, Dietz AS, Rhayem Y, Bancos I, Feuchtinger A, Chortis V, Gilligan LC, Ludwig P, Riester A, Asbach E, Hughes BA, O'Neil DM, Bidlingmaier M, ... ... Biehl M, et al. Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism. Jci Insight. 2. PMID 28422753 DOI: 10.1172/jci.insight.93136  0.01
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  1
2016 Schulz A, Mokbel B, Biehl M, Hammer B. Inferring feature relevances from metric learning Proceedings - 2015 Ieee Symposium Series On Computational Intelligence, Ssci 2015. 1599-1606. DOI: 10.1109/SSCI.2015.225  1
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  1
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  1
2016 Melchert F, Seiffert U, Biehl M. Functional representation of prototypes in LVQ and relevance learning Advances in Intelligent Systems and Computing. 428: 317-327. DOI: 10.1007/978-3-319-28518-4_28  1
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  1
2016 Mudali D, Biehl M, Leenders KL, Roerdink JBTM. LVQ and SVM classification of FDG-PET brain data Advances in Intelligent Systems and Computing. 428: 205-215. DOI: 10.1007/978-3-319-28518-4_18  1
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  1
2015 de Wiljes OO, van Elburg RA, Biehl M, Keijzer FA. Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution. Frontiers in Computational Neuroscience. 9: 110. PMID 26441620 DOI: 10.3389/fncom.2015.00110  0.01
2015 Yeo L, Adlard N, Biehl M, Juarez M, Smallie T, Snow M, Buckley CD, Raza K, Filer A, Scheel-Toellner D. Expression of chemokines CXCL4 and CXCL7 by synovial macrophages defines an early stage of rheumatoid arthritis. Annals of the Rheumatic Diseases. PMID 25858640 DOI: 10.1136/annrheumdis-2014-206921  1
2015 Hormoz S, Bhanot G, Biehl M, Bilal E, Meyer P, Norel R, Rhrissorrakrai K, Dayarian A. Inter-species inference of gene set enrichment in lung epithelial cells from proteomic and large transcriptomic datasets. Bioinformatics (Oxford, England). 31: 492-500. PMID 25152231 DOI: 10.1093/bioinformatics/btu569  0.01
2015 Dayarian A, Romero R, Wang Z, Biehl M, Bilal E, Hormoz S, Meyer P, Norel R, Rhrissorrakrai K, Bhanot G, Luo F, Tarca AL. Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge. Bioinformatics (Oxford, England). 31: 462-70. PMID 25061067 DOI: 10.1093/bioinformatics/btu490  1
2015 Biehl M, Sadowski P, Bhanot G, Bilal E, Dayarian A, Meyer P, Norel R, Rhrissorrakrai K, Zeller MD, Hormoz S. Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge. Bioinformatics (Oxford, England). 31: 453-61. PMID 24994890 DOI: 10.1093/bioinformatics/btu407  1
2015 Biehl M, Sadowski P, Bhanot G, Bilal E, Dayarian A, Meyer P, Norel R, Rhrissorrakrai K, Zeller MD, Hormoz S. Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge. Bioinformatics (Oxford, England). 31: 453-61. PMID 24994890 DOI: 10.1093/bioinformatics/btu407  1
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  1
2015 Frenay B, Hofmann D, Schulz A, Biehl M, Hammer B. Valid interpretation of feature relevance for linear data mappings Ieee Ssci 2014 - 2014 Ieee Symposium Series On Computational Intelligence - Cidm 2014: 2014 Ieee Symposium On Computational Intelligence and Data Mining, Proceedings. 149-156. DOI: 10.1109/CIDM.2014.7008661  1
2015 Biehl M, Ghio A, Schleif FM. Developments in computational intelligence and machine learning Neurocomputing. DOI: 10.1016/j.neucom.2015.03.062  1
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  1
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  1
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  1
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  1
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  1
2015 de Vries GJ, Pauws S, Biehl M. Facial expression recognition using learning vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9257: 760-771. DOI: 10.1007/978-3-319-23117-4_65  1
2014 Davis CF, Ricketts CJ, Wang M, Yang L, Cherniack AD, Shen H, Buhay C, Kang H, Kim SC, Fahey CC, Hacker KE, Bhanot G, Gordenin DA, Chu A, Gunaratne PH, ... Biehl M, et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell. 26: 319-30. PMID 25155756 DOI: 10.1016/j.ccr.2014.07.014  1
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  1
2014 Biehl M. Prototype-Based Classifiers and Their Application in the Life Sciences Advances in Intelligent Systems and Computing. 295: 121. DOI: 10.1007/978-3-319-07695-9_11  1
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  1
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  0.01
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  1
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  1
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  1
2013 Lange M, Biehl M, Villmann T. Non-euclidean independent component analysis and Oja's learning Esann 2013 Proceedings, 21st European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 125-130.  1
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  1
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  0.01
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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.  1
2010 Quinn JA, Mooij J, Heskes T, Biehl M. Learning of causal relations Esann 2011 Proceedings, 19th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 287-296.  1
2010 Bunte K, Hammer B, Villmann T, Biehl M, Wismüller A. Exploratory observation machine (XOM) with kullback-leibler divergence for dimensionality reduction and visualization Proceedings of the 18th European Symposium On Artificial Neural Networks - Computational Intelligence and Machine Learning, Esann 2010. 87-92.  1
2010 Schneider P, Geweniger T, Schleif FM, Biehl M, Villmann T. Multivariate class labeling in robust soft LVQ Esann 2011 Proceedings, 19th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 17-22.  1
2010 Mwebaze E, Biehl M, Quinn JA. Causal relevance learning for robust classification under interventions Esann 2011 Proceedings, 19th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 315-320.  1
2010 Bunte K, Biehl M, Hammer B. Supervised dimension reduction mappings Esann 2011 Proceedings, 19th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 281-286.  1
2010 Käastner M, Hammer B, Biehl M, Villmann T. Generalized functional relevance learning vector quantization Esann 2011 Proceedings, 19th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 93-98.  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
2008 Rossi F, Biehl M, Bahón CA. Progress in modeling, theory, and application of computational intelligence Neurocomputing. 71: 1117-1119. DOI: 10.1016/j.neucom.2007.12.019  1
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  1
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  1
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  1
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  1
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  1
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  1
2005 Volkmann T, Much F, Biehl M, Kotrla M. Interplay of strain relaxation and chemically induced diffusion barriers: Nanostructure formation in 2D alloys Surface Science. 586: 157-173. DOI: 10.1016/j.susc.2005.05.010  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  1
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  0.01
1991 Biehl M, Opper M. Tilinglike learning in the parity machine. Physical Review. A. 44: 6888-6894. PMID 9905815  0.04
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