| 2009 |
|---|
| 97 | EE | Nikolai Alex,
Alexander Hasenfuss,
Barbara Hammer:
Patch clustering for massive data sets.
Neurocomputing 72(7-9): 1455-1469 (2009) |
| 2008 |
|---|
| 96 | | Luc De Raedt,
Barbara Hammer,
Pascal Hitzler,
Wolfgang Maass:
Recurrent Neural Networks - Models, Capacities, and Applications, 20.01. - 25.01.2008
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008 |
| 95 | EE | Alexander Hasenfuss,
Barbara Hammer,
Fabrice Rossi:
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets.
ANNPR 2008: 1-12 |
| 94 | EE | Marc Strickert,
Petra Schneider,
Jens Keilwagen,
Thomas Villmann,
Michael Biehl,
Barbara Hammer:
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.
ANNPR 2008: 78-89 |
| 93 | | Alexander Hasenfuss,
Barbara Hammer:
Single Pass Clustering and Classification of Large Dissimilarity Datasets.
Artificial Intelligence and Pattern Recognition 2008: 219-223 |
| 92 | | Marc Strickert,
Nese Sreenivasulu,
Thomas Villmann,
Barbara Hammer:
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation.
BIOSIGNALS (2) 2008: 197-203 |
| 91 | EE | Nikolai Alex,
Barbara Hammer:
Parallelizing single patch pass clustering.
ESANN 2008: 227-232 |
| 90 | EE | Alexander Hasenfuss,
Barbara Hammer,
Tina Geweniger,
Thomas Villmann:
Magnification Control in Relational Neural Gas.
ESANN 2008: 325-330 |
| 89 | EE | Banchar Arnonkijpanich,
Barbara Hammer,
Alexander Hasenfuss,
Chidchanok Lursinsap:
Matrix Learning for Topographic Neural Maps.
ICANN (1) 2008: 572-582 |
| 88 | EE | Luc De Raedt,
Barbara Hammer,
Pascal Hitzler,
Wolfgang Maass:
08041 Abstracts Collection -- Recurrent Neural Networks - Models, Capacities, and Applications.
Recurrent Neural Networks 2008 |
| 87 | EE | Luc De Raedt,
Barbara Hammer,
Pascal Hitzler,
Wolfgang Maass:
08041 Summary -- Recurrent Neural Networks - Models, Capacities, and Applications.
Recurrent Neural Networks 2008 |
| 86 | EE | Frank-Michael Schleif,
Thomas Villmann,
Barbara Hammer,
Martijn van der Werff,
André M. Deelder,
R. A. E. M. Tollenaar:
Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers.
Computational Intelligence in Biomedicine and Bioinformatics 2008: 141-167 |
| 85 | EE | Thomas Villmann,
Frank-Michael Schleif,
Markus Kostrzewa,
Axel Walch,
Barbara Hammer:
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.
Briefings in Bioinformatics 9(2): 129-143 (2008) |
| 84 | EE | Frank-Michael Schleif,
Thomas Villmann,
Barbara Hammer:
Prototype based fuzzy classification in clinical proteomics.
Int. J. Approx. Reasoning 47(1): 4-16 (2008) |
| 83 | EE | Aree Witoelar,
Michael Biehl,
Anarta Ghosh,
Barbara Hammer:
Learning dynamics and robustness of vector quantization and neural gas.
Neurocomputing 71(7-9): 1210-1219 (2008) |
| 2007 |
|---|
| 82 | | Michael Biehl,
Barbara Hammer,
Michel Verleysen,
Thomas Villmann:
Similarity-based Clustering and its Application to Medicine and Biology, 25.03. - 30.03.2007
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2007 |
| 81 | | Barbara Hammer,
Pascal Hitzler:
Perspectives of Neural-Symbolic Integration
Springer 2007 |
| 80 | EE | Aree Witoelar,
Michael Biehl,
Anarta Ghosh,
Barbara Hammer:
On the dynamics of Vector Quantization and Neural Gas.
ESANN 2007: 127-132 |
| 79 | EE | Petra Schneider,
Michael Biehl,
Barbara Hammer:
Relevance matrices in LVQ.
ESANN 2007: 37-42 |
| 78 | EE | Barbara Hammer,
Thomas Villmann:
How to process uncertainty in machine learning?.
ESANN 2007: 79-90 |
| 77 | EE | Alexander Hasenfuss,
Barbara Hammer:
Relational Topographic Maps.
IDA 2007: 93-105 |
| 76 | EE | Barbara Hammer,
Alexander Hasenfuss,
Frank-Michael Schleif,
Thomas Villmann,
Marc Strickert,
Udo Seiffert:
Intuitive Clustering of Biological Data.
IJCNN 2007: 1877-1882 |
| 75 | EE | Frank-Michael Schleif,
Thomas Villmann,
Barbara Hammer:
Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra.
IWANN 2007: 1036-1044 |
| 74 | EE | Alexander Hasenfuss,
Barbara Hammer,
Frank-Michael Schleif,
Thomas Villmann:
Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes.
IWANN 2007: 539-546 |
| 73 | EE | Thomas Villmann,
Frank-Michael Schleif,
Erzsébet Merényi,
Barbara Hammer:
Fuzzy Labeled Self-Organizing Map for Classification of Spectra.
IWANN 2007: 556-563 |
| 72 | EE | Barbara Hammer,
Alexander Hasenfuss:
Relational Neural Gas.
KI 2007: 190-204 |
| 71 | EE | Barbara Hammer,
Alessio Micheli,
Alessandro Sperduti:
A general framework for unsupervised preocessing of structured data.
Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 |
| 70 | EE | Michael Biehl,
Barbara Hammer,
Michel Verleysen,
Thomas Villmann:
07131 Abstracts Collection -- Similarity-based Clustering and its Application to Medicine and Biology.
Similarity-based Clustering and its Application to Medicine and Biology 2007 |
| 69 | EE | Michael Biehl,
Barbara Hammer,
Michel Verleysen,
Thomas Villmann:
07131 Summary -- Similarity-based Clustering and its Application to Medicine and Biology.
Similarity-based Clustering and its Application to Medicine and Biology 2007 |
| 68 | EE | Aree Witoelar,
Michael Biehl,
Barbara Hammer:
Learning Vector Quantization: generalization ability and dynamics of competing prototypes.
Similarity-based Clustering and its Application to Medicine and Biology 2007 |
| 67 | EE | Barbara Hammer,
Alexander Hasenfuss:
Relational Clustering.
Similarity-based Clustering and its Application to Medicine and Biology 2007 |
| 66 | EE | Frank-Michael Schleif,
Thomas Villmann,
Barbara Hammer:
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps.
WILF 2007: 563-570 |
| 65 | EE | Barbara Hammer,
Alessio Micheli,
Alessandro Sperduti:
Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.
Perspectives of Neural-Symbolic Integration 2007: 67-94 |
| 64 | EE | Peter Tino,
Barbara Hammer,
Mikael Bodén:
Markovian Bias of Neural-based Architectures With Feedback Connections.
Perspectives of Neural-Symbolic Integration 2007: 95-133 |
| 63 | EE | Frank-Michael Schleif,
Barbara Hammer,
Thomas Villmann:
Margin-based active learning for LVQ networks.
Neurocomputing 70(7-9): 1215-1224 (2007) |
| 62 | EE | Barbara Hammer,
Alexander Hasenfuss,
Thomas Villmann:
Magnification control for batch neural gas.
Neurocomputing 70(7-9): 1225-1234 (2007) |
| 2006 |
|---|
| 61 | EE | Barbara Hammer,
Alexander Hasenfuss,
Frank-Michael Schleif,
Thomas Villmann:
Supervised Batch Neural Gas.
ANNPR 2006: 33-45 |
| 60 | EE | Thomas Villmann,
Udo Seiffert,
Frank-Michael Schleif,
Cornelia Brüß,
Tina Geweniger,
Barbara Hammer:
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.
ANNPR 2006: 46-56 |
| 59 | EE | Thomas Villmann,
Barbara Hammer,
Udo Seiffert:
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing.
BioADIT 2006: 141-159 |
| 58 | EE | Frank-Michael Schleif,
Thomas Elssner,
Markus Kostrzewa,
Thomas Villmann,
Barbara Hammer:
Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps.
CBMS 2006: 919-924 |
| 57 | EE | Udo Seiffert,
Barbara Hammer,
Samuel Kaski,
Thomas Villmann:
Neural networks and machine learning in bioinformatics - theory and applications.
ESANN 2006: 521-532 |
| 56 | EE | Frank-Michael Schleif,
Barbara Hammer,
Thomas Villmann:
Margin based Active Learning for LVQ Networks.
ESANN 2006: 539-544 |
| 55 | EE | Barbara Hammer,
Alexander Hasenfuss,
Thomas Villmann:
Magnification control for batch neural gas.
ESANN 2006: 7-12 |
| 54 | EE | Barbara Hammer,
Thomas Villmann,
Frank-Michael Schleif,
Cornelia Albani,
Wieland Hermann:
Learning Vector Quantization Classification with Local Relevance Determination for Medical Data.
ICAISC 2006: 603-612 |
| 53 | EE | Thomas Villmann,
Barbara Hammer,
Frank-Michael Schleif,
Tina Geweniger,
Tom Fischer,
Marie Cottrell:
Prototype Based Classification Using Information Theoretic Learning.
ICONIP (2) 2006: 40-49 |
| 52 | EE | Thomas Villmann,
Frank-Michael Schleif,
Barbara Hammer:
Comparison of relevance learning vector quantization with other metric adaptive classification methods.
Neural Networks 19(5): 610-622 (2006) |
| 51 | EE | Marie Cottrell,
Barbara Hammer,
Alexander Hasenfuss,
Thomas Villmann:
Batch and median neural gas.
Neural Networks 19(6-7): 762-771 (2006) |
| 50 | EE | Thomas Villmann,
Barbara Hammer,
Frank-Michael Schleif,
Tina Geweniger,
Wieland Hermann:
Fuzzy classification by fuzzy labeled neural gas.
Neural Networks 19(6-7): 772-779 (2006) |
| 49 | EE | Anarta Ghosh,
Michael Biehl,
Barbara Hammer:
Performance analysis of LVQ algorithms: A statistical physics approach.
Neural Networks 19(6-7): 817-829 (2006) |
| 48 | EE | Thomas Villmann,
Frank-Michael Schleif,
Barbara Hammer:
Prototype-based fuzzy classification with local relevance for proteomics.
Neurocomputing 69(16-18): 2425-2428 (2006) |
| 47 | EE | Marc Strickert,
Udo Seiffert,
Nese Sreenivasulu,
Winfriede Weschke,
Thomas Villmann,
Barbara Hammer:
Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis.
Neurocomputing 69(7-9): 651-659 (2006) |
| 46 | EE | Michael Biehl,
Anarta Ghosh,
Barbara Hammer:
Learning vector quantization: The dynamics of winner-takes-all algorithms.
Neurocomputing 69(7-9): 660-670 (2006) |
| 2005 |
|---|
| 45 | EE | Michael Biehl,
Anarta Ghosh,
Barbara Hammer:
The dynamics of Learning Vector Quantization.
ESANN 2005: 13-18 |
| 44 | EE | Barbara Hammer,
Andreas Rechtien,
Marc Strickert,
Thomas Villmann:
Relevance learning for mental disease classification.
ESANN 2005: 139-144 |
| 43 | EE | Barbara Hammer,
Thomas Villmann:
Classification using non-standard metrics.
ESANN 2005: 303-316 |
| 42 | EE | Katharina Tluk von Toschanowitz,
Barbara Hammer,
Helge Ritter:
Relevance determination in reinforcement learning.
ESANN 2005: 369-374 |
| 41 | EE | Frank-Michael Schleif,
Thomas Villmann,
Barbara Hammer:
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data.
WILF 2005: 290-296 |
| 40 | EE | Barbara Hammer,
Alessio Micheli,
Alessandro Sperduti:
Universal Approximation Capability of Cascade Correlation for Structures.
Neural Computation 17(5): 1109-1159 (2005) |
| 39 | EE | Barbara Hammer,
Craig Saunders,
Alessandro Sperduti:
Special issue on neural networks and kernel methods for structured domains.
Neural Networks 18(8): 1015-1018 (2005) |
| 38 | EE | Barbara Hammer,
Marc Strickert,
Thomas Villmann:
Supervised Neural Gas with General Similarity Measure.
Neural Processing Letters 21(1): 21-44 (2005) |
| 37 | EE | Barbara Hammer,
Marc Strickert,
Thomas Villmann:
On the Generalization Ability of GRLVQ Networks.
Neural Processing Letters 21(2): 109-120 (2005) |
| 36 | EE | Marie Cottrell,
Barbara Hammer,
Thomas Villmann:
New Aspects in Neurocomputing.
Neurocomputing 63: 1-3 (2005) |
| 35 | EE | Kai Gersmann,
Barbara Hammer:
Improving iterative repair strategies for scheduling with the SVM.
Neurocomputing 63: 271-292 (2005) |
| 34 | EE | Marc Strickert,
Barbara Hammer,
Sebastian Blohm:
Unsupervised recursive sequence processing.
Neurocomputing 63: 69-97 (2005) |
| 33 | EE | Marc Strickert,
Barbara Hammer:
Merge SOM for temporal data.
Neurocomputing 64: 39-71 (2005) |
| 32 | EE | Bhaskar DasGupta,
Barbara Hammer:
On approximate learning by multi-layered feedforward circuits.
Theor. Comput. Sci. 348(1): 95-127 (2005) |
| 2004 |
|---|
| 31 | EE | Barbara Hammer,
Brijnesh J. Jain:
Neural methods for non-standard data.
ESANN 2004: 281-292 |
| 30 | EE | Marc Strickert,
Barbara Hammer:
Self-organizing context learning.
ESANN 2004: 39-44 |
| 29 | EE | Barbara Hammer,
Marc Strickert,
Thomas Villmann:
Relevance LVQ versus SVM.
ICAISC 2004: 592-597 |
| 28 | EE | Barbara Hammer,
Alessio Micheli,
Alessandro Sperduti,
Marc Strickert:
Recursive self-organizing network models.
Neural Networks 17(8-9): 1061-1085 (2004) |
| 27 | EE | Barbara Hammer,
Alessio Micheli,
Alessandro Sperduti,
Marc Strickert:
A general framework for unsupervised processing of structured data.
Neurocomputing 57: 3-35 (2004) |
| 2003 |
|---|
| 26 | EE | Kai Gersmann,
Barbara Hammer:
Improving iterative repair strategies for scheduling with the SVM.
ESANN 2003: 235-240 |
| 25 | EE | Marc Strickert,
Barbara Hammer:
Unsupervised Recursive Sequence Processing.
ESANN 2003: 27-32 |
| 24 | EE | Barbara Hammer,
Thomas Villmann:
Mathematical Aspects of Neural Networks.
ESANN 2003: 59-72 |
| 23 | EE | Barbara Hammer,
Peter Tiño:
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines.
Neural Computation 15(8): 1897-1929 (2003) |
| 22 | EE | Peter Tiño,
Barbara Hammer:
Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
Neural Computation 15(8): 1931-1957 (2003) |
| 21 | EE | Thomas Villmann,
Erzsébet Merényi,
Barbara Hammer:
Neural maps in remote sensing image analysis.
Neural Networks 16(3-4): 389-403 (2003) |
| 20 | | Barbara Hammer,
Kai Gersmann:
A Note on the Universal Approximation Capability of Support Vector Machines.
Neural Processing Letters 17(1): 43-53 (2003) |
| 2002 |
|---|
| 19 | | Barbara Hammer,
Thomas Villmann:
Batch-RLVQ.
ESANN 2002: 295-300 |
| 18 | | Barbara Hammer,
Jochen J. Steil:
Perspectives on learning with recurrent neural networks.
ESANN 2002: 357-368 |
| 17 | | Barbara Hammer,
Alessio Micheli,
Alessandro Sperduti:
A general framework for unsupervised processing of structured data.
ESANN 2002: 389-394 |
| 16 | EE | Peter Tiño,
Barbara Hammer:
Architectural Bias in Recurrent Neural Networks - Fractal Analysis.
ICANN 2002: 1359-1364 |
| 15 | EE | Barbara Hammer,
Marc Strickert,
Thomas Villmann:
Learning Vector Quantization for Multimodal Data.
ICANN 2002: 370-376 |
| 14 | EE | Barbara Hammer,
Andreas Rechtien,
Marc Strickert,
Thomas Villmann:
Rule Extraction from Self-Organizing Networks.
ICANN 2002: 877-883 |
| 13 | EE | Barbara Hammer,
Thomas Villmann:
Generalized relevance learning vector quantization.
Neural Networks 15(8-9): 1059-1068 (2002) |
| 2001 |
|---|
| 12 | EE | Thorsten Bojer,
Barbara Hammer,
Daniel Schunk,
Katharina Tluk von Toschanowitz:
Relevance determination in Learning Vector Quantization.
ESANN 2001: 271-276 |
| 11 | EE | Barbara Hammer,
Thomas Villmann:
Input pruning for neural gas architectures.
ESANN 2001: 283-288 |
| 10 | EE | Marc Strickert,
Thorsten Bojer,
Barbara Hammer:
Generalized Relevance LVQ for Time Series.
ICANN 2001: 677-683 |
| 9 | EE | Barbara Hammer:
On the Generalization Ability of Recurrent Networks.
ICANN 2001: 731-736 |
| 8 | EE | Barbara Hammer:
Generalization Ability of Folding Networks.
IEEE Trans. Knowl. Data Eng. 13(2): 196-206 (2001) |
| 2000 |
|---|
| 7 | EE | Bhaskar DasGupta,
Barbara Hammer:
On Approximate Learning by Multi-layered Feedforward Circuits.
ALT 2000: 264-278 |
| 6 | EE | Barbara Hammer:
Limitations of hybrid systems.
ESANN 2000: 213-218 |
| 5 | EE | Barbara Hammer:
On the approximation capability of recurrent neural networks.
Neurocomputing 31(1-4): 107-123 (2000) |
| 1999 |
|---|
| 4 | EE | Barbara Hammer:
Approximation capabilities of folding networks.
ESANN 1999: 33-38 |
| 1998 |
|---|
| 3 | EE | Barbara Hammer:
Training a sigmoidal network is difficult.
ESANN 1998: 255-260 |
| 2 | | Barbara Hammer:
On the Approximation Capability of Recurrent Neural Networks.
NC 1998: 512-518 |
| 1997 |
|---|
| 1 | | Barbara Hammer:
Generalization of Elman Networks.
ICANN 1997: 409-414 |
