Database Reference
In-Depth Information
[23] Lane, T., and Brodley, C. E., Approaches to online and concept drift for user
identification in computer security, in Proc. 4th International Conference on
Knowledge Discovery and Data Mining , New York, 259-63, 1998.
[24] Last, M., Klein, Y., and Kandel, A., Knowledge discovery in time series
databases, IEEE Transactions on Systems, Man, and Cybernetics , Vol. 31:
Part B, No. 1, 160-9, 2001.
[25] Last, M., Maimon, O., and Minkov, E., Improving stability of decision trees,
International Journal of Pattern Recognition and Artificial Intelligence , 16
(2), 145-59, 2002.
[26] Maimon, O., and Last, M., Knowledge Discovery and Data Mining, the Info-
Fuzzy Network (IFN) Methodology , Kluwer Academic Publishers, 2000.
[27] Martinez, T., Consistency and generalization in incrementally trained
connectionist networks, in Proc. International Symposium on Circuits and
Systems , 706-9, 1990.
[28] Mangasarian, O. L., and Solodov, M. V., Backpropagation convergence via
deterministic nonmonotone perturbed mininization, in Advances in Neural
Information Processing Systems , 6, 383-90, 1994.
[29] Mitchell, T. M., Machine Learning , Carnegie Mellon University, McGraw
Hill, 1997.
[30] Montgomery, D. C., and Runger, G. C., Applied Statistics and Probability for
Engineers, 2nd edition, Wiley, 1999.
[31] Nouira, R., and Fouet, J.M., A knowledge based tool for the incremental
construction, validation and refinement of large knowledge bases, in
Preliminary Proceedings of Workshop on Validation, Verification and
Refinement of BKS (ECAI96), 1996.
[32] Ohsie, D., Hasanat, M. D., Stolfo, S. J., and Da Silva, S., Performance of
Incremental Update in Database Rule Processing, In J. Widom, S.
Chakravarthy (Eds.): Proc. Fourth International Workshop on Research
Issues in Data Engineering: Active Database Systems, Houston, Texas,
February 14-15, 10-18, 1994 .
[33] Shen, W. M., An active and semi-incremental algorithm for learning decision
lists, Technical Report, USC-ISI-97, Information Sciences Institute,
University of Southern California, 1997.
[34] Shen, W. M., Bayesian probability theory - A general method for machine
learning, from MCC-Carnot-101-93, Microelectronics and Computer
Technology Corp., Austin, TX, 1997.
[35] Utgoff, P.E., An improved algorithm for incremental induction of decision
trees, in Machine Learning: Proc. 11th International Conference , 318-25,
1994.
[36] Utgoff, P. E., Decision tree induction based on efficient tree restructuring,
Technical Report 95-18, Department of Computer Science, University of
Massachusetts, 1995.
[37] Widmer, G., and Kubat, M., Learning in the presence of concept drift and
hidden contexts, Machine Learning , 23(1), 69-101, 1996.
[38] Yao, Y., Estimating the number of change points via Schwartz' criterion,
Statistics and Probability Letters , 181-9, 1988.
Search WWH ::




Custom Search