| 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 |
0.607 |
|
| 2021 |
Bohnsack KS, Kaden M, Abel J, Saralajew S, Villmann T. The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. Entropy (Basel, Switzerland). 23. PMID 34682081 DOI: 10.3390/e23101357 |
0.341 |
|
| 2020 |
Ravichandran J, Kaden M, Saralajew S, Villmann T. Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability Neurocomputing. 403: 121-132. DOI: 10.1016/J.Neucom.2019.12.131 |
0.411 |
|
| 2019 |
Bauer H, Herrmann M, Villmann T. Neural maps and topographic vector quantization. Neural Networks : the Official Journal of the International Neural Network Society. 12: 659-676. PMID 12662676 DOI: 10.1016/S0893-6080(99)00027-1 |
0.339 |
|
| 2017 |
Villmann T, Bohnsack A, Kaden M. Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning Journal of Artificial Intelligence and Soft Computing Research. 7: 65-81. DOI: 10.1515/jaiscr-2017-0005 |
0.43 |
|
| 2017 |
Nebel D, Kaden M, Villmann A, Villmann T. Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning Neurocomputing. 268: 42-54. DOI: 10.1016/J.Neucom.2016.12.091 |
0.476 |
|
| 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 |
0.669 |
|
| 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 |
0.632 |
|
| 2016 |
Bohnsack A, Domaschke K, Kaden M, Lange M, Villmann T. Learning matrix quantization and relevance learning based on Schatten-p-norms Neurocomputing. 192: 104-114. DOI: 10.1016/J.Neucom.2015.12.109 |
0.5 |
|
| 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 |
0.656 |
|
| 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 |
0.406 |
|
| 2016 |
Nebel D, Villmann T. Optimization of statistical evaluation measures for classification by median learning vector quantization Advances in Intelligent Systems and Computing. 428: 281-291. DOI: 10.1007/978-3-319-28518-4_25 |
0.347 |
|
| 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 |
0.57 |
|
| 2015 |
Zok T, Antczak M, Riedel M, Nebel D, Villmann T, Lukasiak P, Blazewicz J, Szachniuk M. Building the Library of RNA 3D Nucleotide Conformations Using the Clustering Approach International Journal of Applied Mathematics and Computer Science. 25: 689-700. DOI: 10.1515/Amcs-2015-0050 |
0.303 |
|
| 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 |
0.416 |
|
| 2015 |
Villmann T, Kaden M, Lange M, Sturmer P, Hermann W. Precision-recall-optimization in learning vector quantization classifiers for improved medical classification systems Ieee Ssci 2014 - 2014 Ieee Symposium Series On Computational Intelligence - Cidm 2014: 2014 Ieee Symposium On Computational Intelligence and Data Mining, Proceedings. 71-77. DOI: 10.1109/CIDM.2014.7008150 |
0.359 |
|
| 2015 |
Nebel D, Hammer B, Frohberg K, Villmann T. Median variants of learning vector quantization for learning of dissimilarity data Neurocomputing. 169: 295-305. DOI: 10.1016/J.Neucom.2014.12.096 |
0.492 |
|
| 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 |
0.636 |
|
| 2015 |
Villmann T, Haase S, Kaden M. Kernelized vector quantization in gradient-descent learning Neurocomputing. 147: 83-95. DOI: 10.1016/J.Neucom.2013.11.048 |
0.775 |
|
| 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 |
0.579 |
|
| 2014 |
Kaden M, Lange M, Nebel D, Riedel M, Geweniger T, Villmann T. Aspects in classification learning - Review of recent developments in learning vector quantization Foundations of Computing and Decision Sciences. 39: 79-105. DOI: 10.2478/Fcds-2014-0006 |
0.774 |
|
| 2014 |
Villmann T, Kaden M, Nebel D, Riedel M. Lateral enhancement in adaptive metric learning for functional data Neurocomputing. 131: 23-31. DOI: 10.1016/J.Neucom.2013.07.049 |
0.519 |
|
| 2014 |
Kaden M, Riedel M, Hermann W, Villmann T. Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines Soft Computing. 19: 2423-2434. DOI: 10.1007/s00500-014-1496-1 |
0.429 |
|
| 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 |
0.477 |
|
| 2014 |
Kaden M, Hermann W, Villmann T. Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment Advances in Intelligent Systems and Computing. 295: 77-87. DOI: 10.1007/978-3-319-07695-9_7 |
0.41 |
|
| 2014 |
Lange M, Nebel D, Villmann T. Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization Advances in Intelligent Systems and Computing. 295: 259-269. DOI: 10.1007/978-3-319-07695-9_25 |
0.319 |
|
| 2014 |
Fischer L, Nebel D, Villmann T, Hammer B, Wersing H. Rejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches Advances in Intelligent Systems and Computing. 295: 109-118. DOI: 10.1007/978-3-319-07695-9_10 |
0.364 |
|
| 2014 |
Lange M, Nebel D, Villmann T. Non-euclidean principal component analysis for matrices by Hebbian learning Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8467: 77-88. DOI: 10.1007/978-3-319-07173-2_8 |
0.313 |
|
| 2013 |
Geweniger T, Fischer L, Kaden M, Lange M, Villmann T. Clustering by fuzzy neural gas and evaluation of fuzzy clusters. Computational Intelligence and Neuroscience. 2013: 165248. PMID 24396342 DOI: 10.1155/2013/165248 |
0.742 |
|
| 2013 |
Lange M, Kastner M, Villmann T. About analysis and robust classification of searchlight fMRI-data using machine learning classifiers Proceedings of the International Joint Conference On Neural Networks. DOI: 10.1109/IJCNN.2013.6706990 |
0.359 |
|
| 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 |
0.476 |
|
| 2013 |
Nebel D, Hammer B, Villmann T. A median variant of generalized learning vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8227: 19-26. DOI: 10.1007/978-3-642-42042-9_3 |
0.368 |
|
| 2013 |
Kästner M, Riedel M, Strickert M, Hermann W, Villmann T. Border-sensitive learning in kernelized learning vector quantization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7902: 357-366. DOI: 10.1007/978-3-642-38679-4_35 |
0.434 |
|
| 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 |
0.642 |
|
| 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 |
0.622 |
|
| 2012 |
Bauer HU, Villmann T. Growing a hypercubical output space in a self-organizing feature map. Ieee Transactions On Neural Networks. 8: 218-26. PMID 18255626 DOI: 10.1109/72.557659 |
0.366 |
|
| 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 |
0.514 |
|
| 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 |
0.546 |
|
| 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 |
0.588 |
|
| 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 |
0.753 |
|
| 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 |
0.657 |
|
| 2011 |
Schleif FM, Villmann T, Hammer B, Schneider P. Efficient Kernelized prototype based classification. International Journal of Neural Systems. 21: 443-57. PMID 22131298 DOI: 10.1142/S012906571100295X |
0.512 |
|
| 2011 |
Villmann T, Haase S. Divergence-based vector quantization. Neural Computation. 23: 1343-92. PMID 21299418 DOI: 10.1162/Neco_A_00110 |
0.803 |
|
| 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 |
0.605 |
|
| 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 |
0.791 |
|
| 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 |
0.675 |
|
| 2010 |
Geweniger T, Zülke D, Hammer B, Villmann T. Median fuzzy c-means for clustering dissimilarity data Neurocomputing. 73: 1109-1116. DOI: 10.1016/J.Neucom.2009.11.020 |
0.741 |
|
| 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 |
0.617 |
|
| 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 |
0.8 |
|
| 2009 |
Schleif FM, Villmann T, Kostrzewa M, Hammer B, Gammerman A. Cancer informatics by prototype networks in mass spectrometry. Artificial Intelligence in Medicine. 45: 215-28. PMID 18778925 DOI: 10.1016/J.Artmed.2008.07.018 |
0.323 |
|
| 2009 |
Schleif F, Villmann T, Ongyerth M. Supervised data analysis and reliability estimation with exemplary application for spectral data Neurocomputing. 72: 3590-3601. DOI: 10.1016/J.Neucom.2008.12.040 |
0.395 |
|
| 2009 |
Simmuteit S, Schleif F, Villmann T, Hammer B. Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints Knowledge and Information Systems. 25: 327-343. DOI: 10.1007/S10115-009-0249-4 |
0.325 |
|
| 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 |
0.653 |
|
| 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 |
0.482 |
|
| 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 |
0.551 |
|
| 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 |
0.634 |
|
| 2008 |
Villmann T, Schleif FM, Kostrzewa M, Walch A, Hammer B. Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics. 9: 129-43. PMID 18334515 DOI: 10.1093/Bib/Bbn009 |
0.437 |
|
| 2008 |
Strickert M, Schleif F, Seiffert U, Villmann T. Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data Inteligencia Artificial. 12. DOI: 10.4114/Ia.V12I37.956 |
0.335 |
|
| 2008 |
Villmann T, Liebers C, Bergmann B, Gumz A, Geyer M. Investigation of psycho-physiological interactions between patient and therapist during a psychodynamic therapy and their relation to speech using in terms of entropy analysis using a neural network approach New Ideas in Psychology. 26: 309-325. DOI: 10.1016/J.Newideapsych.2007.07.010 |
0.311 |
|
| 2008 |
Villmann T, Hammer B, Schleif FM, Hermann W, Cottrell M. Fuzzy classification using information theoretic learning vector quantization Neurocomputing. 71: 3070-3076. DOI: 10.1016/J.Neucom.2008.04.048 |
0.532 |
|
| 2008 |
Schleif F, Villmann T, Hammer B. Prototype based fuzzy classification in clinical proteomics International Journal of Approximate Reasoning. 47: 4-16. DOI: 10.1016/J.Ijar.2007.03.005 |
0.349 |
|
| 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 |
0.586 |
|
| 2007 |
Merényi E, Jain A, Villmann T. Explicit magnification control of self-organizing maps for "forbidden" data. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 18: 786-97. PMID 17526344 DOI: 10.1109/Tnn.2007.895833 |
0.322 |
|
| 2007 |
Schleif F, Hammer B, Villmann T. Margin-based active learning for LVQ networks Neurocomputing. 70: 1215-1224. DOI: 10.1016/J.Neucom.2006.10.149 |
0.519 |
|
| 2007 |
Hammer B, Hasenfuss A, Villmann T. Magnification control for batch neural gas Neurocomputing. 70: 1225-1234. DOI: 10.1016/J.Neucom.2006.10.147 |
0.457 |
|
| 2007 |
Hermann W, Villmann T, Kühn HJ, Baum P, Reichel G, Wagner A, Günther P. Neurophysiologic classification of Wilson’s disease by using cluster analysis Clinical Neurophysiology. 118. DOI: 10.1016/J.Clinph.2006.11.112 |
0.301 |
|
| 2007 |
Villmann T, Schleif FM, Merenyi E, Hammer B. Fuzzy labeled self-organizing map for classification of spectra Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4507: 556-563. |
0.365 |
|
| 2006 |
Villmann T, Hammer B, Schleif F, Geweniger T, Herrmann W. Fuzzy classification by fuzzy labeled neural gas. Neural Networks : the Official Journal of the International Neural Network Society. 19: 772-9. PMID 16815673 DOI: 10.1016/j.neunet.2006.05.026 |
0.784 |
|
| 2006 |
Villmann T, Claussen JC. Magnification control in self-organizing maps and neural gas. Neural Computation. 18: 446-69. PMID 16378522 DOI: 10.1162/089976606775093918 |
0.486 |
|
| 2006 |
Villmann T, Schleif F, Hammer B. Prototype-based fuzzy classification with local relevance for proteomics Neurocomputing. 69: 2425-2428. DOI: 10.1016/J.Neucom.2006.02.003 |
0.442 |
|
| 2006 |
Strickert M, Seiffert U, Sreenivasulu N, Weschke W, Villmann T, Hammer B. Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis Neurocomputing. 69: 651-659. DOI: 10.1016/J.Neucom.2005.12.004 |
0.383 |
|
| 2006 |
Hammer B, Villmann T, Schleif FM, Albani C, Hermann W. Learning vector quantization classification with local relevance determination for medical data Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4029: 603-612. DOI: 10.1007/11785231_63 |
0.323 |
|
| 2006 |
Villmann T, Hammer B, Seiffert U. Perspectives of self-adapted self-organizing clustering in organic computing Lecture Notes in Computer Science. 141-159. DOI: 10.1007/11613022_14 |
0.301 |
|
| 2005 |
Villmann T, Schleif F, Hammer B. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks : the Official Journal of the International Neural Network Society. 19: 610-22. PMID 16343848 DOI: 10.1016/J.Neunet.2005.07.013 |
0.444 |
|
| 2005 |
Hermann W, Günther P, Wagner A, Villmann T. Classification of Wilson's disease based on neurophysiological parameters Nervenarzt. 76: 733-739. PMID 15959750 DOI: 10.1007/S00115-004-1843-Z |
0.323 |
|
| 2005 |
Hammer B, Strickert M, Villmann T. Supervised Neural Gas with General Similarity Measure Neural Processing Letters. 21: 21-44. DOI: 10.1007/S11063-004-3255-2 |
0.447 |
|
| 2005 |
Hammer B, Strickert M, Villmann T. On the Generalization Ability of GRLVQ Networks Neural Processing Letters. 21: 109-120. DOI: 10.1007/S11063-004-1547-1 |
0.506 |
|
| 2004 |
Villmann T, Villmann B, Slowik V. Evolutionary algorithms with neighborhood cooperativeness according to neural maps Neurocomputing. 57: 151-169. DOI: 10.1016/J.Neucom.2004.01.012 |
0.346 |
|
| 2003 |
Villmann T, Merényi E, Hammer B. Neural maps in remote sensing image analysis. Neural Networks : the Official Journal of the International Neural Network Society. 16: 389-403. PMID 12672434 DOI: 10.1016/S0893-6080(03)00021-2 |
0.377 |
|
| 2002 |
Hammer B, Villmann T. Generalized relevance learning vector quantization. Neural Networks : the Official Journal of the International Neural Network Society. 15: 1059-68. PMID 12416694 DOI: 10.1016/S0893-6080(02)00079-5 |
0.449 |
|
| 2002 |
Villmann T. Evolutionary algorithms using a neural network like migration scheme Integrated Computer-Aided Engineering. 9: 25-35. DOI: 10.3233/Ica-2002-9102 |
0.43 |
|
| 1998 |
Villmann T, Bauer H. Applications of the growing self-organizing map Neurocomputing. 21: 91-100. DOI: 10.1016/S0925-2312(98)00037-X |
0.328 |
|
| 1997 |
Villmann T, Der R, Herrmann M, Martinetz TM. Topology preservation in self-organizing feature maps: exact definition and measurement. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 8: 256-66. PMID 18255630 DOI: 10.1109/72.557663 |
0.342 |
|
| 1997 |
Der R, Herrmann M, Villmann T. Time behavior of topological ordering in self-organizing feature mapping Biological Cybernetics. 77: 419-427. DOI: 10.1007/S004220050401 |
0.329 |
|
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