Generalized relevance LVQ with correlation measures for biological data

ESANN 2005 Proceedings - 13th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 331-338

  Strickert, Marc ^a   Sreenivasulu, Nese ^a   Weschke, Winfriede ^a   Seiffert, Udo ^a   Villmann, Thomas ^b  

^a Pattern Recognition Group   (Germany)

^b UNIVERSITY OF LEIPZIG   (Germany)

Author keywords

Adaptive metrics; Correlation measure; GRLVQ; Learning vector quantization; Prototype based learning

Indexed keywords

ADAPTIVE METRICS; CORRELATION MEASURES; GRLVQ; LEARNING VECTOR QUANTIZATION; PROTOTYPE-BASED LEARNING;

CHARACTER RECOGNITION; CORRELATION METHODS; EXPERIMENTS; GENE EXPRESSION; NEURAL NETWORKS;

VECTOR QUANTIZATION;

EID: 84887004970     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. FL3 Handwritten Symbol Database - Subset of the NIST Special Database 1, NIST. ftp://sequoyah.ncsl.nist.gov/pub/databases/data.
2. I. Fischer. Similarity-based neural networks for applications in computational molecular biology. Advances in Intelligent Data Analysis V, 5(3):208-218, 2003.
3. B. Hammer, M. Strickert, and T. Villmann. Relevance LVQ versus SVM. In L. Rutkowski, J. Siekmann, R. Tadeusiewicz, and L. Zadeh, editors, Artificial Intelligence and Softcomputing, volume 3070 of Springer Lecture Notes in Artificial Intelligence, pages 592-597. Springer, 2004.
4. B. Hammer and T. Villmann. Generalized Relevance Learning Vector Quantization. Neural Networks, 15:1059-1068, 2002.
5. S. Kaski. Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Transactions on Neural Networks, 12:936-947, 2001.
6. T. Kohonen. Self-Organizing Maps. Springer-Verlag, Berlin, 3rd edition, 2001.
7. T. Martinetz, S. Berkovich, and K. Schulten. "Neural-gas" network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, 4(4):558-569, 1993.
8. F. Rossi, B. Conan-Guez, and A. E. Golli. Clustering functional data with the SOM algorithm. In Proceedings of ESANN 2004, pages 305-312, Bruges, Belgium, April 2004.
9. A. Sato and K. Yamada. Generalized Learning Vector Quantization. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems 7 (NIPS), volume 7, pages 423-429. MIT Press, 1995.
10. T. Villmann, F. Schleif, and B. Hammer. Supervised Neural Gas and Relevance Learning in Learning Vector Quantization. In T. Yamakawa, editor, Proc. of the Workshop on Self-Organizing Networks (WSOM), pages 47-52, Kyushu Institute of Technology, 2003.