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 |