Supervised generative models for learning dissimilarity data

22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings

Volumn , Issue , 2014, Pages 35-40

  Nebel, D ^a   Hammer, B ^b   Villmann, T ^a  

^a UNIVERSITY OF APPLIED SCIENCES MITTWEIDA   (Germany)

^b BIELEFELD UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; LEARNING SYSTEMS; NEURAL NETWORKS;

AFFINITY PROPAGATION; DISSIMILARITY DATUM; DISSIMILARITY MEASURES; EXEMPLAR-BASED; GENERATIVE MODEL; NON-EUCLIDEAN; SPARSE MODELS; STATE OF THE ART;

ARTIFICIAL INTELLIGENCE;

EID: 84962026959     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Brendan J J. Frey and Delbert Dueck. Clustering by passing messages between data points. Science, pages 972-976, January 2007.
2. Sambu Seo and Klaus Obermayer. Soft learning vector quantization. Neural Computation, 15(7):1589-1604, 2003.
3. Orion Penner, Peter Grassberger, and Maya Paczuski. Sequence alignment, mutual information, and dissimilarity measures for constructing phylogenies. PLOS ONE, 6(1), 2011.
4. Thomas Maier, Stefan Klepel, Uwe Renner, and Markus Kostrzewa. Fast and reliable maldi-tof ms- based microorganism identification. Nature Methods, 3, 2006.
5. Piers J Ingram, Michael PH Stumpf, and Jaroslav Stark. Network motifs: structure does not determine function. BMC Genomics, 7:108, 2006.
6. Brijnesh J. Jain and Klaus Obermayer. Structure spaces. The Journal of Machine Learning Research, 10:2667-2714, 2009.
7. Thomas Gärtner and Gemma C. Garriga. Guest editors' introduction: special issue on mining and learning with graphs. Machine Learning, 75(1):1-2, 2009.
8. G. Da San Martino and A. Sperduti. Mining structured data. Computational Intelligence Magazine, IEEE, 5:42-49, 2010.
9. Vanya Van Belle and Paulo Lisboa. Research directions in interpretable machine learning models. In European Symposium on Artificial Neuronal Networks, Computational Intelligence and Machiene Learning, 2013.
10. Shibin Parameswaran and Kilian Q. Weinberger. Large margin multi-task metric learning. In John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, and Aron Culotta, editors, NIPS, pages 1867-1875. Curran Associates, Inc., 2010.
11. Petra Schneider, Michael Biehl, and Barbara Hammer. Adaptive relevance matrices in learning vector quantization. Neural Computation, 21(12):3532-3561, 2009.
12. A. Kai Qin and Ponnuthurai N. Suganthan. A novel kernel prototype-based learning algorithm. In ICPR (4), pages 621-624, 2004.
13. Teuvo Kohonen. Self-Organizing Maps. Springer, 2001.
14. Koby Crammer, Ran Gilad-Bachrach, Amir Navot, and Naftali Tishby. Margin analysis of the lvq algorithm. NIPS, pages 462-469, 2002.
15. Sambu Seo, Mathias Bode, and Klaus Obermayer. Soft nearest prototype classification. IEEE TRANSACTIONS ON NEURAL NETWORKS, 14(2):390-398, March 2003.
16. T. Villmann, S. Haase, and M. Kaden. Kernelized vector quantization in gradient-descent learning. Neurocomputing, page in press, 2014.
17. Barbara Hammer, Daniela Hofmann, Frank-Michael Schleif, and Xibin Zhu. Learning vector quantization for (dis-)similarities. Neurocomputing (in press).
18. P. Schneider, M. Biehl, and B. Hammer. Distance learning in discriminative vector quantization. Neural Computation, 21:2942-2969, 2009.
19. Elzbieta Pekalska and Robert P. W. Duin. The Dissimilarity Representation for Pattern Recognition: Foundations and Applications (Series in Machine Perception and Artifical Intelligence). 2005.
20. Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi, and L. Cazzanti. Similarity-based classification: Concepts and algorithms. Journal of Machine Learning Research, 10:747-776, 2009.
21. D. Nebel, B. Hammer, and T. Villmann. About learning of supervised generative models for dissimilarity data. Machine Learning Reports, 7(MLR-05-2013):1-19, 2013. ISSN:1865-3960, http://www.techfak.uni-bielefeld.de/~fschleif/mlr/mlr-05-2013.pdf.
22. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39:1-38, 1977.
23. R. Neal and G. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, pages 355-368. MIT Press, 1999.
24. D. Nebel and T. Villmann. A median variant of generalized learning vector quantization. In M. Lee, A. Hirose, Z.-G. Hou, and R.M. Kil, editors, Proceedings of International Conference on Neural Information Processing (ICONIP), volume II of LNCS, pages 19-26, Berlin, 2013. Springer-Verlag.
25. A. Sato and K. Yamada. Generalized learning vector quantization. NIPS, 1995.