Mining data by query-based error-propagation

Lecture Notes in Computer Science

Volumn 3610, Issue PART I, 2005, Pages 1224-1233

  Lai, Liang Bin ^a   Chang, Ray I ^a   Kouh, Jen Shaing ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA ACQUISITION; MATHEMATICAL MODELS; NEURAL NETWORKS;

ERROR-PROPAGATION; NOISY DATA; SET CARDINALITY;

DATA MINING;

EID: 26844484295     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11539087_162     Document Type: Conference Paper

References (18)

1. D. Ben-Arieh, M. Chopra, and M. Z. Bleyberg, "Data mining application for real-time distributed shop floor control," Proceedings of 1998 IEEE International Conference on Systems, Man, and Cybernetics, San Diego: IEEE, pp. 2738-2743, 1998.
2. T. Mitchell, ".Machine learning," New York: WCB/McGraw-Hill, 1997.
3. Ray-I Chang and Pei-Yung Hsiao, "Unsupervised query-based learning of neural networks using selective-attention and self-regulation," IEEE Trans. Neural Networks, vol.8, no.2, pp.205-217, 1997.
4. D. Ridley, and F. Llaugel, "Moving-window spectral neural-network feedforward process control," IEEE Transactions on Engineering Management, 47(3), 393-402, 2000.
5. M.W. Craven, and J. W. Shavlik, "Using neural networks for data mining," Future generation computer systems, 13(2-3) 211-229, 1997.
6. Hongjun Lu, R. Setiono, and Huan Liu, "Effective data mining using neural networks," Knowledge and Data Engineering, IEEE Transactions on, Volume: 8 Issue: 6, pp. 957 -961, 1996.
7. J. Hwang, J. Choi, S. Oh, R. Marks II, "Query-based learning applied to partially trained multilayer perceptrons," IEEE Transactions on Neural Networks, Vol. 2, pp.131-136, 1991.
8. Ray-I Chang, Pei-Yung Hsiao, "VLSI circuit placement with rectilinear modules using three-layer force directed self-organizing maps," IEEE Trans. Neural Networks, vol.8, no.5, pp. 1049-1064, September 1997.
9. J. W. Han and M. Kamber, "Data Mining: Concepts and Techniques," San Francisco: Morgan Kaufmann Publishers, 2001.
10. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal Representations by Error Propagation," In Paralleled Distributed Processing, Vol. 1, pp. 318-362, Cambridge, MA: MIT Press, 1986.
11. L. Valiant, "A theory of the learnable," Communications of ACM, 27, pp.1134-1142, 1984.
12. T. Oates, and D. Jensen, "The Effects of Training Set Size on Decision Tree Complexity," In Proceedings of The Fourteenth International Conference on Machine Learning, pp. 254-262, 1997.
13. E. B. Baum, "Neural-net algorithms that learn in polynomial time from examples and queries," IEEE Transactions on Neural Networks, Vol. 1, pp.5-19, 1991.
14. A. Linden, and J. Kindermann, "Inversion of multilayer nets," IJCNN, International Joint Conference on Neural Networks, vol.2, pp.425-430, 1989.
15. R. Reed, R. J. Marks II and S. Oh, "Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter," IEEE Transactions on Neural Networks, Volume: 6 Issue: 3, pp.529-538, 1995.
16. C. Blake, E. Keogh, C. J. Merz, UCI Repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html, Irvine, University of California, Department of Information and Computer Science, 1998.
17. R. Kohavi and F. Provost, "Glossary of Terms. Machine Learning," 30(2/3):271-274, 1998.
18. J. M. DeLeo, and S. J. Rosenfeld, "Essential roles for receiver operating characteristic (ROC) methodology in classifier neural network applications," in Proc. Int. Joint Conf. Neural Networks, Vol.4, pp. 2730-2731, 2001.