Evolving vector quantization for classification of on-line data streams

2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008

Volumn , Issue , 2008, Pages 779-784

  Lughofer, Edwin ^a  

^a JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION TECHNIQUE; DATA SAMPLE; FEATURE DATA; FEATURE SPACE; HIGH-DIMENSIONAL; LABEL INFORMATION; LEARNING VECTOR QUANTIZATION; MATRIX; ON-DEMAND; ON-LINE QUALITY CONTROL; ONLINE DATA; PRODUCTION PROCESS; TRAINING PROCESS;

ARTIFICIAL INTELLIGENCE; IMAGE PROCESSING; PROCESS CONTROL; QUALITY FUNCTION DEPLOYMENT; TOTAL QUALITY MANAGEMENT;

VECTOR QUANTIZATION;

EID: 70449562136     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIMCA.2008.47     Document Type: Conference Paper

References (13)

1. S. Bharitkar and D. Filev. An online learning vector quantization algorithm. In in Proc. of Sixth International Symposium on Signal Processing and its Applications vol. 2, pages 394-397, 2001.
2. L. Breiman, J. Friedman, C. Stone, and R. Olshen. Classification and Regression Trees. Chapman and Hall, Boca Raton, 1993.
3. G. A. Carpenter and S. Grossberg. Adaptive resonance theory (art). In M. A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 79-82. MIT Press, Cambridge, MA, 1995.
4. C. Diehl and G. Cauwenberghs. SVM incremental learning, adaptation and optimization. In Proceedings of the International Joint Conference on Neural Networks Vol. 4, pages 2685-2690, 2003.
5. R. Gray. Vector quantization. IEEE ASSP Magazine, pages 4-29, 1984.
6. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Verlag, New York, Berlin, Heidelberg, Germany, 2001.
7. T. Kohonen. An introduction to neural computing. Neural Networks, 1:3-16, 1988.
8. T. Kohonen, G. Barna, and R. Chrisley. Statistical pattern recognition with neural networks: Benchmarking studies. In Proc. of the IEEE International Conference on Neural Networks, 1988.
9. E. Lughofer. Extensions of vector quantization for incremental clustering. Pattern Recognition, 41(3):995-1011, 2008.
10. F. Poirier and A. Ferrieux. DVQ: Dynamic vector quantization - an incremental LVQ. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, pages 1333-1336. Elsevier Science Publishers B.V., North-Holland, 1991.
11. D. Sannen, M. Nuttin, J. Smith, M. Tahir, E. Lughofer, and C. Eitzinger. An interactive self-adaptive on-line image classification framework. In A. Gasteratos, M. Vincze, and J. Tsotsos, editors, Proceedings of ICVS 2008, volume 5008 of LNCS, pages 173-180. Springer, Santorini Island, Greece, 2008.
12. B. Scholkopf and A. Smola. Learning with Kernels - Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, London, England, 2002.
13. J.-H. Wang and W.-D. Sun. Online learning vector quantization: a harmonic competition approach based on conservation network. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 29(5):642-653, 1999.