Fuzzy supervised self-organizing map for semi-supervised vector quantization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7267 LNAI, Issue PART 1, 2012, Pages 256-265

  Kästner, Marika ^a   Villmann, Thomas ^a  

^a UNIVERSITY OF APPLIED SCIENCES MITTWEIDA   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS LABELS; DATA SPACE; DISSIMILARITY MEASURES; FUZZY CLASS; FUZZY CLASSIFICATION; GRADIENT DESCENT; IMAGE CUBE; NEURAL GAS; ORIGINAL ALGORITHMS; REMOTE SENSING DATA; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SUPERVISED CLASSIFICATION; SUPERVISED SELF-ORGANIZING MAP; VECTOR QUANTIZERS;

ARTIFICIAL INTELLIGENCE; CONFORMAL MAPPING; REMOTE SENSING; SOFT COMPUTING; SUPERVISED LEARNING; VISUALIZATION;

VECTOR QUANTIZATION;

EID: 84861082187     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-29347-4_30     Document Type: Conference Paper

References (21)

1. Bauer, H.-U., Herrmann, M., Villmann, T.: Neural maps and topographic vector quantization. Neural Networks 12(4-5), 659-676 (1999)
2. Geweniger, T., Zühlke, D., Hammer, B., Villmann, T.: Median fuzzy c-means for clustering dissimilarity data. Neurocomputing 73(7-9), 1109-1116 (2010)
3. Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Networks 15(8-9), 1059-1068 (2002)
4. Heskes, T.: Energy functions for self-organizing maps. In: Oja, E., Kaski, S. (eds.) Kohonen Maps, pp. 303-316. Elsevier, Amsterdam (1999)
5. Kästner, M., Villmann, T.: Functional relevance learning in generalized learning vector quantization. Machine Learning Reports 5(MLR-01-2011), 81-89 (2011), http://www.techfak.uni-bielefeld.de/~fschleif/mlr/ mlr-01-2011.pdf ISSN:1865-3960
6. Kästner, M., Villmann, T.: Fuzzy supervised neural gas for semi-supervised vector quantization - theoretical aspects. Machine Learning Reports 5(MLR-02-2011), 1-16 (2011), http://www.techfak.uni-bielefeld.de/ ~fschleif/mlr/mlr$-$-011.pdf ISSN:1865-3960
7. Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (1995) (Second Extended Edition 1997)
8. p norm for time series and its application to self-organizing maps. In: Cottrell, M. (ed.) Proc. of Workshop on Self- Organizing Maps, WSOM 2005, Paris, Sorbonne, pp. 733-740 (2005)
9. Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: 'Neural-gas' network for vector quantization and its application to time-series prediction. IEEE Trans. on Neural Networks 4(4), 558-569 (1993)
10. Midenet, S., Grumbach, A.: Learning associations by self-organizatiom: the LASSO model. Neurocomputing 6, 343-361 (1994)
11. Pekalska, E., Duin, R.: The Dissimilarity Representation for Pattern Recognition: Foundations and Applications. World Scientific (2006)
12. Sato, A., Yamada, K.: Generalized learning vector quantization. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Proceedings of the 1995 Conference on Advances in Neural Information Processing Systems, vol. 8, pp. 423-429. MIT Press, Cambridge (1996)
13. Schleif, F.-M., Villmann, T., Hammer, B., Schneider, P., Biehl, M.: Generalized Derivative Based Kernelized Learning Vector Quantization. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds.) IDEAL 2010. LNCS, vol. 6283, pp. 21-28. Springer, Heidelberg (2010)
14. Schneider, P., Hammer, B., Biehl, M.: Adaptive relevance matrices in learning vector quantization. Neural Computation 21, 3532-3561 (2009)
15. Villmann, T., Der, R., Herrmann, M., Martinetz, T.: Topology Preservation in Self- Organizing Feature Maps: Exact Definition and Measurement. IEEE Transactions on Neural Networks 8(2), 256-266 (1997)
16. Villmann, T., Haase, S.: Divergence based vector quantization. Neural Computation 23(5), 1343-1392 (2011)
17. Villmann, T., Hammer, B., Schleif, F.-M., Geweniger, T., Herrmann, W.: Fuzzy classification by fuzzy labeled neural gas. Neural Networks 19, 772-779 (2006)
18. Villmann, T., Merényi, E., Hammer, B.: Neural maps in remote sensing image analysis. Neural Networks 16(3-4), 389-403 (2003)
19. Villmann, T., Schleif, F.-M.: Functional vector quantization by neural maps. In: Chanussot, J. (ed.) Proceedings of First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2009), pp. 1-4. IEEE Press (2009) ISBN: 978-1-4244-4948-4
20. Villmann, T., Schleif, F.-M., Merenyi, E., Hammer, B.: Fuzzy Labeled Self- Organizing Map for Classification of Spectra. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 556-563. Springer, Heidelberg (2007)
21. Villmann, T., Seiffert, U., Schleif, F.-M., Brüß, C., Geweniger, T., Hammer, B.: Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In: Schwenker, F., Marinai, S. (eds.) ANNPR 2006. LNCS (LNAI), vol. 4087, pp. 46-56. Springer, Heidelberg (2006)