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 |
|
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