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Publications of Author

1. Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer
   Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. [Citation Graph (0, 0)][DBLP]
   CBMS, 2006, pp:919-924 [Conf]
   
2. Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert
   Fuzzy image segmentation with Fuzzy Labelled Neural Gas. [Citation Graph (0, 0)][DBLP]
   ESANN, 2006, pp:563-568 [Conf]
   
3. Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
   Margin based Active Learning for LVQ Networks. [Citation Graph (0, 0)][DBLP]
   ESANN, 2006, pp:539-544 [Conf]
   
4. Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann
   Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. [Citation Graph (0, 0)][DBLP]
   ICAISC, 2006, pp:603-612 [Conf]
   
5. Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell
   Prototype Based Classification Using Information Theoretic Learning. [Citation Graph (0, 0)][DBLP]
   ICONIP (2), 2006, pp:40-49 [Conf]
   
6. Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
   Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. [Citation Graph (0, 0)][DBLP]
   WILF, 2005, pp:290-296 [Conf]
   
7. Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann
   Supervised Batch Neural Gas. [Citation Graph (0, 0)][DBLP]
   ANNPR, 2006, pp:33-45 [Conf]
   
8. Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer
   Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. [Citation Graph (0, 0)][DBLP]
   ANNPR, 2006, pp:46-56 [Conf]
   
9. Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
   Comparison of relevance learning vector quantization with other metric adaptive classification methods. [Citation Graph (0, 0)][DBLP]
   Neural Networks, 2006, v:19, n:5, pp:610-622 [Journal]
   
10. Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Wieland Hermann
    Fuzzy classification by fuzzy labeled neural gas. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2006, v:19, n:6-7, pp:772-779 [Journal]
    
11. Marc Gerhard, Soren-Oliver Deininger, Frank-Michael Schleif
    Statistical Classification and Visualization of MALDI-Imaging Data. [Citation Graph (0, 0)][DBLP]
    CBMS, 2007, pp:403-405 [Conf]
    
12. Thomas Villmann, Frank-Michael Schleif, Erzsébet Merényi, Barbara Hammer
    Fuzzy Labeled Self-Organizing Map for Classification of Spectra. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:556-563 [Conf]
    
13. 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. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:1036-1044 [Conf]
    
14. Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann
    Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:539-546 [Conf]
    
15. Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
    Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. [Citation Graph (0, 0)][DBLP]
    WILF, 2007, pp:563-570 [Conf]
    
16. Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
    Margin-based active learning for LVQ networks. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2007, v:70, n:7-9, pp:1215-1224 [Journal]
    
17. Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
    Prototype-based fuzzy classification with local relevance for proteomics. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2006, v:69, n:16-18, pp:2425-2428 [Journal]
    
18. 
    Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. [Citation Graph (, )][DBLP]
    
    
19. 
    Advances in pre-processing and model generation for mass spectrometric data analysis. [Citation Graph (, )][DBLP]
    
    
20. 
    Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. [Citation Graph (, )][DBLP]
    
    
21. 
    Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. [Citation Graph (, )][DBLP]
    
    
22. 
    Metric adaptation for supervised attribute rating. [Citation Graph (, )][DBLP]
    
    
23. 
    Generalized matrix learning vector quantizer for the analysis of spectral data. [Citation Graph (, )][DBLP]
    
    
24. 
    Divergence Based Online Learning in Vector Quantization. [Citation Graph (, )][DBLP]
    
    
25. 
    Association Learning in SOMs for Fuzzy-Classification. [Citation Graph (, )][DBLP]
    
    
26. 
    Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. [Citation Graph (, )][DBLP]
    
    
27. 
    Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. [Citation Graph (, )][DBLP]
    
    
28. 
    Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure. [Citation Graph (, )][DBLP]
    
    
29. 
    Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. [Citation Graph (, )][DBLP]
    
    
30. 
    Generalized Derivative Based Kernelized Learning Vector Quantization. [Citation Graph (, )][DBLP]
    
    
31. 
    Intuitive Clustering of Biological Data. [Citation Graph (, )][DBLP]
    
    
32. 
    Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. [Citation Graph (, )][DBLP]
    
    
33. 
    The Mathematics of Divergence Based Online Learning in Vector Quantization. [Citation Graph (, )][DBLP]
    
    
34. 
    Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. [Citation Graph (, )][DBLP]
    
    
35. 
    Cancer informatics by prototype networks in mass spectrometry. [Citation Graph (, )][DBLP]
    
    
36. 
    Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. [Citation Graph (, )][DBLP]