
Thomas Villmann
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2020 – today
- 2020
- [j58]Jensun Ravichandran, Marika Kaden, Sascha Saralajew, Thomas Villmann:
Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability. Neurocomputing 403: 121-132 (2020) - [j57]Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert
, Michael Biehl
, Friedrich Melchert
:
Learning vector quantization and relevances in complex coefficient space. Neural Comput. Appl. 32(24): 18085-18099 (2020) - [c163]Seyedfakhredin Musavishavazi, Mehrdad Mohannazadeh Bakhtiari, Thomas Villmann:
A Mathematical Model for Optimum Error-Reject Trade-Off for Learning of Secure Classification Models in the Presence of Label Noise During Training. ICAISC (1) 2020: 547-554 - [c162]Sascha Saralajew, Lars Holdijk, Thomas Villmann:
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. NeurIPS 2020
2010 – 2019
- 2019
- [j56]Sebastian Bittrich
, Marika Kaden, Christoph Leberecht, Florian Kaiser, Thomas Villmann, Dirk Labudde:
Application of an interpretable classification model on Early Folding Residues during protein folding. BioData Min. 12(1): 1:1-1:16 (2019) - [c161]Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann:
Statistical physics of learning and inference. ESANN 2019 - [c160]Jensun Ravichandran, Sascha Saralajew, Thomas Villmann:
DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. ESANN 2019 - [c159]Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann:
Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. ICAISC (2) 2019: 443-454 - [c158]Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann:
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. NeurIPS 2019: 2788-2799 - [c157]Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden:
Investigation of Activation Functions for Generalized Learning Vector Quantization. WSOM+ 2019: 179-188 - [c156]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks. WSOM+ 2019: 189-199 - [c155]Tina Geweniger, Thomas Villmann:
Variants of Fuzzy Neural Gas. WSOM+ 2019: 261-270 - [c154]Thomas Villmann, Marika Kaden, Szymon Wasik, Mateusz Kudla, Kaja Gutowska
, Andrea Villmann, Jacek Blazewicz
:
Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization. WSOM+ 2019: 324-333 - [i10]Thomas Villmann, John Ravichandran, Andrea Villmann, David Nebel, Marika Kaden:
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison. CoRR abs/1901.05995 (2019) - [i9]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks. CoRR abs/1902.00577 (2019) - 2018
- [j55]Thomas Villmann, Marika Kaden, Wieland Hermann, Michael Biehl
:
Learning vector quantization classifiers for ROC-optimization. Comput. Stat. 33(3): 1173-1194 (2018) - [c153]Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann:
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. ESANN 2018 - [c152]Falko Lischke, Thomas Neumann, Sven Hellbach, Thomas Villmann, Hans-Joachim Böhme:
Direct Incorporation of L_1 -Regularization into Generalized Matrix Learning Vector Quantization. ICAISC (1) 2018: 657-667 - [c151]Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann:
Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. ICAISC (1) 2018: 724-735 - [c150]Thomas Villmann, Tina Geweniger:
Multi-class and Cluster Evaluation Measures Based on Rényi and Tsallis Entropies and Mutual Information. ICAISC (1) 2018: 736-749 - [c149]Thomas Villmann:
Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks. IJCCI 2018: 15-21 - [i8]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Prototype-based Neural Network Layers: Incorporating Vector Quantization. CoRR abs/1812.01214 (2018) - 2017
- [j54]David Nebel, Marika Kaden, Andrea Villmann, Thomas Villmann:
Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning. Neurocomputing 268: 42-54 (2017) - [j53]Thomas Villmann, Andrea Bohnsack, Marika Kaden:
Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning. J. Artif. Intell. Soft Comput. Res. 7(1): 65 (2017) - [c148]Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zühlke:
Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. ESANN 2017 - [c147]Mohammad Mohammadi, Michael Biehl
, Andrea Villmann, Thomas Villmann:
Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. ICAISC (1) 2017: 131-142 - [c146]Sascha Saralajew, Thomas Villmann:
Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. IJCNN 2017: 1756-1765 - [c145]Thomas Villmann, Michael Biehl
, Andrea Villmann, Sascha Saralajew:
Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. WSOM 2017: 69-76 - [c144]Michiel Straat
, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert
, Michael Biehl
, Friedrich Melchert:
Prototypes and matrix relevance learning in complex fourier space. WSOM 2017: 139-144 - [c143]Marika Kaden, David Nebel, Friedrich Melchert, Andreas Backhaus, Udo Seiffert
, Thomas Villmann:
Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities. WSOM 2017: 220-226 - [c142]Tina Geweniger, Thomas Villmann:
Relational and median variants of Possibilistic Fuzzy C-Means. WSOM 2017: 234-240 - 2016
- [j52]Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann:
Learning matrix quantization and relevance learning based on Schatten-p-norms. Neurocomputing 192: 104-114 (2016) - [c141]Michael Biehl
, Barbara Hammer
, Thomas Villmann:
Prototype-based Models for the Supervised Learning of Classification Schemes. Astroinformatics 2016: 129-138 - [c140]Marika Kaden, David Nebel, Thomas Villmann:
Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. ESANN 2016 - [c139]Thomas Villmann, Marika Kaden, David Nebel, Andrea Bohnsack:
Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization. ICAISC (2) 2016: 125-133 - [c138]Sascha Saralajew, David Nebel, Thomas Villmann:
Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. ICONIP (3) 2016: 362-371 - [c137]Sascha Saralajew, Thomas Villmann:
Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. IJCNN 2016: 2672-2679 - [c136]Thomas Villmann, Marika Kaden, Andrea Bohnsack, J.-M. Villmann, T. Drogies, Sascha Saralajew, Barbara Hammer
:
Self-Adjusting Reject Options in Prototype Based Classification. WSOM 2016: 269-279 - [c135]David Nebel, Thomas Villmann:
Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization. WSOM 2016: 281-291 - [c134]Matthias Gay, Marika Kaden, Michael Biehl
, Alexander Lampe, Thomas Villmann:
Complex Variants of GLVQ Based on Wirtinger's Calculus. WSOM 2016: 293-303 - [i7]Gyan Bhanot, Michael Biehl
, Thomas Villmann, Dietlind Zühlke
:
Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261). Dagstuhl Reports 6(6): 88-110 (2016) - 2015
- [j51]Tomasz Zok, Maciej Antczak, Martin Riedel, David Nebel, Thomas Villmann, Piotr Lukasiak, Jacek Blazewicz, Marta Szachniuk:
Building the Library of Rna 3D Nucleotide Conformations Using the Clustering Approach. Int. J. Appl. Math. Comput. Sci. 25(3): 689-700 (2015) - [j50]Thomas Villmann, Sven Haase, Marika Kaden:
Kernelized vector quantization in gradient-descent learning. Neurocomputing 147: 83-95 (2015) - [j49]Mandy Lange, Michael Biehl
, Thomas Villmann:
Non-Euclidean principal component analysis by Hebbian learning. Neurocomputing 147: 107-119 (2015) - [j48]David Nebel, Barbara Hammer
, Kathleen Frohberg, Thomas Villmann:
Median variants of learning vector quantization for learning of dissimilarity data. Neurocomputing 169: 295-305 (2015) - [j47]Marika Kaden, Martin Riedel, Wieland Hermann, Thomas Villmann:
Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines. Soft Comput. 19(9): 2423-2434 (2015) - [c133]Thomas Villmann:
Sophisticated LVQ Classification Models - Beyond Accuracy Optimization. BrainComp 2015: 116-130 - [c132]Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl
:
Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. CAIP (2) 2015: 772-782 - [c131]Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann:
Learning matrix quantization and variants of relevance learning. ESANN 2015 - [c130]David Nebel, Thomas Villmann:
Median-LVQ for classification of dissimilarity data based on ROC-optimization. ESANN 2015 - [c129]Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann:
Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms. ICAISC (1) 2015: 403-414 - [c128]Michael Biehl
, Barbara Hammer
, Frank-Michael Schleif, Petra Schneider, Thomas Villmann:
Stationarity of Matrix Relevance LVQ. IJCNN 2015: 1-8 - [p3]Davide Bacciu, Paulo J. G. Lisboa, Alessandro Sperduti, Thomas Villmann:
Probabilistic Modeling in Machine Learning. Handbook of Computational Intelligence 2015: 545-575 - 2014
- [j46]Barbara Hammer
, Thomas Villmann:
Special issue on new challenges in neural computation 2012. Neurocomputing 131: 1 (2014) - [j45]Thomas Villmann, Marika Kaden, David Nebel, Martin Riedel:
Lateral enhancement in adaptive metric learning for functional data. Neurocomputing 131: 23-31 (2014) - [c127]Thomas Villmann, Marika Kaden, Mandy Lange, Paul Sturmer, Wieland Hermann:
Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems. CIDM 2014: 71-77 - [c126]Kristin Domaschke, André Roßberg, Thomas Villmann:
Utilization of Chemical Structure Information for Analysis of Spectra Composites. ESANN 2014 - [c125]Marika Kaden, Wieland Hermann, Thomas Villmann:
Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. ESANN 2014 - [c124]Mandy Lange, Dietlind Zühlke, Olaf Holz, Thomas Villmann:
Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. ESANN 2014 - [c123]David Nebel, Barbara Hammer, Thomas Villmann:
Supervised Generative Models for Learning Dissimilarity Data. ESANN 2014 - [c122]Frank-Michael Schleif, Peter Tiño, Thomas Villmann:
Recent trends in learning of structured and non-standard data. ESANN 2014 - [c121]Mandy Lange, David Nebel, Thomas Villmann:
Non-euclidean Principal Component Analysis for Matrices by Hebbian Learning. ICAISC (1) 2014: 77-88 - [c120]Frank-Michael Schleif, Thomas Villmann, Xibin Zhu:
High Dimensional Matrix Relevance Learning. ICDM Workshops 2014: 661-667 - [c119]Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Böhme:
Find Rooms for Improvement: Towards Semi-automatic Labeling of Occupancy Grid Maps. ICONIP (3) 2014: 543-552 - [c118]Marika Kaden, Wieland Hermann, Thomas Villmann:
Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment. WSOM 2014: 77-87 - [c117]Tina Geweniger, Frank-Michael Schleif, Thomas Villmann:
Probabilistic Prototype Classification Using t-norms. WSOM 2014: 99-108 - [c116]Lydia Fischer, David Nebel, Thomas Villmann, Barbara Hammer
, Heiko Wersing:
Rejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches. WSOM 2014: 109-118 - [c115]Barbara Hammer
, David Nebel, Martin Riedel, Thomas Villmann:
Generative versus Discriminative Prototype Based Classification. WSOM 2014: 123-132 - [c114]Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Böhme:
Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps. WSOM 2014: 133-143 - [c113]Mathias Klingner, Sven Hellbach, Martin Riedel, Marika Kaden, Thomas Villmann, Hans-Joachim Böhme:
RFSOM - Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation. WSOM 2014: 157-166 - [c112]Mandy Lange, David Nebel, Thomas Villmann:
Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization. WSOM 2014: 259-269 - [e4]Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange:
Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014. Advances in Intelligent Systems and Computing 295, Springer 2014, ISBN 978-3-319-07694-2 [contents] - 2013
- [j44]Tina Geweniger, Lydia Fischer, Marika Kaden, Mandy Lange, Thomas Villmann:
Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters. Comput. Intell. Neurosci. 2013: 165248:1-165248:10 (2013) - [j43]Derong Liu, Charles Anderson, Ahmad Taher Azar, Giorgio Battistelli, Eduardo Bayro-Corrochano, Cristiano Cervellera, David A. Elizondo, Maurizio Filippone, Giorgio Gnecco
, Xiaolin Hu, Tingwen Huang, Weifeng Liu, Wenlian Lu, Ana Maria Madureira, Igor Skrjanc, Thomas Villmann, Q. M. Jonathan Wu, Shengli Xie, Dong Xu:
Editorial A Successful Change From TNN to TNNLS and a Very Successful Year. IEEE Trans. Neural Networks Learn. Syst. 24(1): 1-7 (2013) - [c111]Michael Biehl
, Barbara Hammer
, Thomas Villmann:
Distance Measures for Prototype Based Classification. BrainComp 2013: 100-116 - [c110]Marc Strickert, Barbara Hammer
, Thomas Villmann, Michael Biehl
:
Regularization and improved interpretation of linear data mappings and adaptive distance measures. CIDM 2013: 10-17 - [c109]Tina Geweniger, Marika Kästner, Thomas Villmann:
Border sensitive fuzzy vector quantization in semi-supervised learning. ESANN 2013 - [c108]Marika Kästner, Marc Strickert, Thomas Villmann:
A sparse kernelized matrix learning vector quantization model for human activity recognition. ESANN 2013 - [c107]Mandy Lange, Michael Biehl, Thomas Villmann:
Non-Euclidean independent component analysis and Oja's learning. ESANN 2013 - [c106]Martin Riedel, Fabrice Rossi, Marika Kästner, Thomas Villmann:
Regularization in relevance learning vector quantization using l1-norms. ESANN 2013 - [c105]Thomas Villmann, Marika Kästner, Andreas Backhaus, Udo Seiffert:
Processing Hyperspectral Data in Machine Learning. ESANN 2013 - [c104]David Nebel, Barbara Hammer
, Thomas Villmann:
A Median Variant of Generalized Learning Vector Quantization. ICONIP (2) 2013: 19-26 - [c103]Mandy Lange, Marika Kästner, Thomas Villmann:
About analysis and robust classification of searchlight fMRI-data using machine learning classifiers. IJCNN 2013: 1-8 - [c102]Marika Kästner, Martin Riedel, Marc Strickert, Wieland Hermann, Thomas Villmann:
Border-Sensitive Learning in Kernelized Learning Vector Quantization. IWANN (1) 2013: 357-366 - [i6]Martin Riedel, Marika Kästner, Fabrice Rossi, Thomas Villmann:
Regularization in Relevance Learning Vector Quantization Using l one Norms. CoRR abs/1310.5095 (2013) - 2012
- [j42]Kerstin Bunte
, Sven Haase, Michael Biehl
, Thomas Villmann:
Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing 90: 23-45 (2012) - [j41]Marika Kästner, Barbara Hammer
, Michael Biehl
, Thomas Villmann:
Functional relevance learning in generalized learning vector quantization. Neurocomputing 90: 85-95 (2012) - [j40]Kerstin Bunte
, Petra Schneider, Barbara Hammer
, Frank-Michael Schleif
, Thomas Villmann, Michael Biehl
:
Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks 26: 159-173 (2012) - [c101]Charles Bouveyron, Barbara Hammer, Thomas Villmann:
Recent developments in clustering algorithms. ESANN 2012 - [c100]Tina Geweniger, Marika Kästner, Mandy Lange, Thomas Villmann:
Modified Conn-Index for the evaluation of fuzzy clusterings. ESANN 2012 - [c99]Marika Kästner, Wieland Hermann, Thomas Villmann:
Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas. ESANN 2012 - [c98]Thomas Villmann, Erzsébet Merényi, William H. Farrand:
Unmixing Hyperspectral Images with Fuzzy Supervised Self-Organizing Maps. ESANN 2012 - [c97]Marika Kästner, Thomas Villmann:
Fuzzy Supervised Self-Organizing Map for Semi-supervised Vector Quantization. ICAISC (1) 2012: 256-265 - [c96]Thomas Villmann, Tina Geweniger, Marika Kästner, Mandy Lange:
Fuzzy Neural Gas for Unsupervised Vector Quantization. ICAISC (1) 2012: 350-358 - [c95]Marika Kästner, David Nebel, Martin Riedel, Michael Biehl
, Thomas Villmann:
Differentiable Kernels in Generalized Matrix Learning Vector Quantization. ICMLA (1) 2012: 132-137 - [c94]Thomas Villmann, Marika Kästner, David Nebel, Martin Riedel:
ICMLA Face Recognition Challenge - Results of the Team Computational Intelligence Mittweida. ICMLA (2) 2012: 592-595 - [c93]Michael Biehl
, Kerstin Bunte
, Frank-Michael Schleif
, Petra Schneider, Thomas Villmann:
Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN 2012: 1-8 - [c92]Gabriele Peters, Kerstin Bunte
, Marc Strickert, Michael Biehl
, Thomas Villmann:
Visualization of processes in self-learning systems. PST 2012: 244-249 - [c91]Michael Biehl
, Marika Kästner, Mandy Lange, Thomas Villmann:
Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects. WSOM 2012: 23-33 - [c90]Thomas Villmann, Sven Haase, Marika Kästner:
Gradient Based Learning in Vector Quantization Using Differentiable Kernels. WSOM 2012: 193-204 - 2011
- [j39]Frank-Michael Schleif
, Thomas Villmann, Barbara Hammer
, Petra Schneider:
Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. 21(6): 443-457 (2011) - [j38]Kerstin Bunte
, Barbara Hammer
, Thomas Villmann, Michael Biehl
, Axel Wismüller:
Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing 74(9): 1340-1350 (2011) - [j37]Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif
, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl
:
Divergence-based classification in learning vector quantization. Neurocomputing 74(9): 1429-1435 (2011) - [j36]Thomas Villmann, Sven Haase:
Divergence-Based Vector Quantization. Neural Comput. 23(5): 1343-1392 (2011) - [c89]Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann:
Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. ESANN 2011 - [c88]Tina Geweniger, Marika Kästner, Thomas Villmann:
Optimization of Parametrized Divergences in Fuzzy c-Means. ESANN 2011 - [c87]Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann:
Generalized functional relevance learning vector quantization. ESANN 2011 - [c86]Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann:
Multivariate class labeling in Robust Soft LVQ. ESANN 2011 - [c85]Marc Strickert, Björn Labitzke, Andreas Kolb, Thomas Villmann:
Multispectral image characterization by partial generalized covariance. ESANN 2011 - [c84]Thomas Villmann, José C. Príncipe, Andrzej Cichocki:
Information theory related learning. ESANN 2011 - [c83]Thomas Villmann, Sven Haase:
Magnification in divergence based neural maps. IJCNN 2011: 437-441 - [c82]Thomas Villmann, Marika Kästner:
Sparse Functional Relevance Learning in Generalized Learning Vector Quantization. WSOM 2011: 79-89 - [c81]Marika Kästner, Andreas Backhaus, Tina Geweniger, Sven Haase, Udo Seiffert
, Thomas Villmann:
Relevance Learning in Unsupervised Vector Quantization Based on Divergences. WSOM 2011: 90-100 - [i5]Michael Biehl
, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, Thomas Villmann:
Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). Dagstuhl Reports 1(8): 67-95 (2011) - 2010
- [j35]Tina Geweniger, Dietlind Zühlke, Barbara Hammer
, Thomas Villmann:
Median fuzzy c-means for clustering dissimilarity data. Neurocomputing 73(7-9): 1109-1116 (2010) - [j34]Stephan Simmuteit, Frank-Michael Schleif
, Thomas Villmann, Barbara Hammer
:
Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowl. Inf. Syst. 25(2): 327-343 (2010) - [j33]Petra Schneider, Kerstin Bunte
, Han Stiekema, Barbara Hammer
, Thomas Villmann, Michael Biehl
:
Regularization in matrix relevance learning. IEEE Trans. Neural Networks 21(5): 831-840 (2010) - [c80]Thomas Villmann, Sven Haase, Frank-Michael Schleif
, Barbara Hammer
, Michael Biehl
:
The Mathematics of Divergence Based Online Learning in Vector Quantization. ANNPR 2010: 108-119 - [c79]Andreas Schierwagen, Thomas Villmann, Alán Alpár, Ulrich Gärtner:
Cluster Analysis of Cortical Pyramidal Neurons Using SOM. ANNPR 2010: 120-130 - [c78]Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismüller:
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. ESANN 2010 - [c77]