Extracting hidden context

Machine Learning

Volumn 32, Issue 2, 1998, Pages 101-126

  Harries, Michael Bonnell ^a   Sammut, Claude ^a   Horn, Kim ^b  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b Horticulture Australia Limited   (Australia)

Author keywords

Batch learning; Concept drift; Context sensitive learning; Contextual clustering; Hidden context

Indexed keywords

DATA ACQUISITION; DATA MINING; DIAGNOSIS; LEARNING ALGORITHMS; ONLINE SYSTEMS; TELECOMMUNICATION NETWORKS; ALGORITHMS; COMPUTER SIMULATION;

BATCH LEARNING; CONCEPT DRIFT; CONTEXT-SENSITIVE LEARNING; CONTEXTUAL CLUSTERING; HIDDEN CONTEXT; CONTEXT SENSITIVE LEARNING;

LEARNING SYSTEMS;

EID: 0032139819     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007420529897     Document Type: Conference Paper

References (23)

1. Aha, D., Kibler, D., & Albert, M. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37-66.
2. Clearwater, S., Cheng, T., & Hirsh, H. (1989). Incremental batch learning. Proceedings of the Sixth International Workshop on Machine Learning (pp 366-370). San Mateo, CA: Morgan Kaufmann.
3. Fayyad, U.M. & Irani, K.B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (pp 1022-1027). San Mateo, CA: Morgan Kaufmann.
4. Fayyad, U.M., Piatsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery: An overview. In Fayyad, U.M., Piatsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Ed.), Advances in Knowledge Discovery and Data Mining. Cambridge, MA: MIT Press.
5. Harries, M. & Horn, K. (1995). Detecting concept drift in financial time series prediction using symbolic machine learning. Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence (pp 91-98). Singapore: World Scientific.
6. Harries, M. & Horn, K. (in press). Learning stable concepts in a changing world. In G. Antoniou, A. Chose and M. Truczszinski (Ed.), LNAI 1359: Learning and Reasoning with Complex Representations. Springer-Verlag.
7. Katz, A.J., Gately, M.T., & Collins, D.R., C. (1990). Robust classifiers without robust features. Neural Computation, 2(4), 412-479.
8. Kilander, F. & Jansson, C.G. (1993). COBBIT - a control procedure for COBWEB in the presence of concept drift. Proceedings of the Sixth European Conference on Machine Learning (pp 244-261). Berlin: Springer-Verlag.
9. Kubat, M. (1989). Floating approximation in time-varying knowledge bases. Pattern Recognition Letters, 10, 223-227.
10. Kubat, M. (1996). Second tier for decision trees. Proceedings of the Thirteenth International Conference on Machine Learning (pp. 293-301). San Francisco, CA: Morgan Kaufmann.
11. Kubat, M. & Widmer, G. (1995). Adapting to drift in continuous domains. Proceedings of the Eighth European Conference on Machine Learning (pp. 307-310). Berlin: Springer-Verlag.
12. Michalski, R.S. (1987). How to learn imprecise concepts: A method employing a two-tiered knowledge representation for learning. Proceedings of the Fourth International Workshop on Machine Learning (pp. 50-58). Los Altos, CA: Morgan Kaufmann.
13. Pratt, Y.P., Mostow, J., & Kamm, C.A. (1991). Direct transfer of learned information among neural networks. Proceedings of the Ninth National Conference on Artificial Intelligence (pp. 584-589). Menlo Park, CA: AAAI Press.
14. Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
15. Rissanen, J. (1983). A universal prior for integers and estimation by minimum description length. Annals of Statistics, 11(2), 416-431.
16. Salganicoff, M. (1993). Density adaptive learning and forgetting. Proceedings of the Tenth International Conference on Machine Learning (pp. 276-283). San Mateo, CA: Morgan Kaufmann.
17. Sammut, C., Hurst, S., Kedzier, D., & Michie, D. (1992). Learning to fly. Proceedings of the Ninth International Conference on Machine Learning (pp. 385-393). San Mateo, CA: Morgan Kaufmann.
18. Schlimmer, J. & Granger, Jr., R. (1986). Incremental learning from noisy data. Machine Learning, 1(3), 317-354.
19. Turney, P.D. (1993). Exploiting context when learning to classify. Proceedings of the Sixth European Conference on Machine Learning (pp. 402-407). Berlin: Springer-Verlag.
20. Turney, P.D. & Halasz, M. (1993). Contextual normalization applied to aircraft gas turbine engine diagnosis. Journal of Applied Intelligence 3, 109-129.
21. Widmer, G. (1997). Tracking Context Changes through Meta-Learning. Machine Learning 27(3), 259-286.
22. Widmer, G. & Kubat, M. (1993). Effective learning in dynamic environments by explicit concept tracking. Proceedings of the Sixth European Conference on Machine Learning (pp. 227-243). Berlin: Springer-Verlag.
23. Widmer, G. & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69-101.