Robust Induction of Process Models from Time-Series Data

Proceedings, Twentieth International Conference on Machine Learning

Volumn 1, Issue , 2003, Pages 432-439

  Langley, Pat ^a   George, Dileep ^a   Bay, Stephen ^a   Saito, Kazumi ^b  

^a STANFORD UNIVERSITY   (United States)

^b NTT Communication Science Laboratories   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; DATA REDUCTION; ECONOMIC AND SOCIAL EFFECTS; ECOSYSTEMS; NUMERICAL METHODS; PARAMETER ESTIMATION; TIME SERIES ANALYSIS;

IPM ALGORITHM; NUMERIC CONDITION;

ROBUSTNESS (CONTROL SYSTEMS);

EID: 1942452377     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Astrom, K. J., & Eykhoff, P. (1971). System identification - A survey. Automatica, 7, 123-167.
2. Bay, S. D., Shapiro, D. G., & Langley, P. (2002). Revising engineering models: Combining computational discovery with knowledge. Proceedings of the Thirteenth European Conference on Machine Learning (pp. 10-22). Helsinki, Finland.
3. Bradley, E., Easley, M., & Stolle, R. (2001). Reasoning about nonlinear system identification. Artificial Intelligence, 133, 139-188.
4. Chan, H., & Sutanto, D. (2000). A new battery model for use with battery energy storage systems and electric vehicle power systems. Proceedings of the IEEE Power Engineering Society Winter Conference.
5. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. London: Chapman & Hall.
6. Farquhar, A. (1994). A qualitative physics compiler. Proceedings of the Twelfth National Conference on Artificial Intelligence (pp. 1168-1174). AAAI Press.
7. Forbus, K. D. (1984). Qualitative process theory. Artificial Intelligence, 24, 85-168.
8. Koza, J. R., Mydlowec, W., Lanza, G., Yu, J., & Keane, M. A. (2001). Reverse engineering and automatic synthesis of metabolic pathways from observed data using genetic programming. Pacific Symposium on Biocomputing, 6, 434-445.
9. Langley, P. (1981). Data-driven discovery of physical laws. Cognitive Science, 5, 31-54.
10. Langley, P. (2000). The computational support of scientific discovery. International Journal of Human-Computer Studies, 53, 393-410.
11. Langley, P., Sanchez, J., Todorovski, L., & Dzeroski, S. (2002). Inducing process models from continuous data. Proceedings of the Nineteenth International Conference on Machine Learning (pp. 347-354). Sydney: Morgan Kaufmann.
12. Saito, K., & Nakano, R. (1997). Law discovery using neural networks. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (pp. 1078-1083). Yokohama: Morgan Kaufmann.
13. Schwartz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
14. Suc, D., & Bratko, I. (2002). Qualitative reverse engineering. Proceedings of the Nineteenth International Conference on Machine Learning (pp. 610-617). Sydney: Morgan Kaufmann.
15. Todorovski, L., & Džeroski, S. (1997). Declarative bias in equation discovery. Proceedings of the Fourteenth International Conference on Machine Learning (pp. 376-384). San Francisco: Morgan Kaufmann.
16. Valdés-Pérez, R. E. (1995). Machine discovery in chemistry: New results. Artificial Intelligence, 74, 191-201.
17. Washio, T., Motoda, H., & Niwa, Y. (2000). Enhancing the plausibility of law equation discovery. Proceedings of the Seventeenth International Conference on Machine Learning (pp. 1127-1134). San Francisco: Morgan Kaufmann.