Database Reference
In-Depth Information
28. Minium, E.W., Clarke, R.B., and Coladarci, T. (1999). Elements of Statistical
Reasoning , Wiley, New York.
29. Mitchell, T.M. (1997). Machine Learning , McGraw Hill.
30. Montgomery, D.C. and Runger, G.C. (1999). Applied Statistics and Proba-
bility for Engineers , Second Edition, Wiley.
31. Nouira, R. and Fouet, J.-M. (1996). A Knowledge Based Tool for the Incre-
mental construction, Validation and Refinement of Large Knowledge Bases.
Preliminary Proceedings of Workshop on validation, verification and refine-
ment of BKS (ECAI96 ).
32. Ohsie, D., Dean, H.M., Stolfo, S.J., and Silva, S.D. (1995). Performance of
Incremental Update in Database Rule Processing.
33. Shen, W.-M. (1997). An Active and Semi-Incremental Algorithm for
Learning Decision Lists. Technical Report , USC-ISI-97, Information Sci-
ences Institute, University of Southern California, 1997. Available at:
http://www.isi.edu/ shen/active-cdl4.ps.
34. Shen, W.-M. (1997). Bayesian Probability Theory — A General
Method for Machine Learning. From MCC-Carnot-101-93. Microelectron-
ics and Computer Technology Corporation , Austin, TX. Available at:
http://www.isi.edu/ shen/Bayes-ML.ps.
35. Utgoff, P.E. and Clouse, J.A. (1996). A Kolmogorov-Smirnoff metric for
decision tree induction. Technical Report 96-3 , Department of computer
science, University of Massachusetts. Available at: ftp://ftp.cs.umass.edu/
pub/techrept/techreport/1996/UM-CS-1996-003.ps
36. Utgoff, P.E. (1994). An improved algorithm for incremental induction of deci-
sion trees. Machine Learning: Proceedings of the Eleventh International Con-
ference , pp. 318-325.
37. Utgoff, P.E. (1995). Decision tree induction based on ecient tree restruc-
turing. Technical Report 95-18, Department of computer science, Univer-
sity of Massachusetts. Available at: ftp://ftp.cs.umass.edu/pub/techrept/
techreport/1995/UM-CS-1995-018.ps
38. Widmer, G. and Kubat, M. (1996). Learning in the Presence of Concept Drift
and Hidden Contexts. Machine Learning , 23 (1), 69-101.
39. Yao, Y. (1988). Estimating the number of change points via Schwartz' crite-
rion. Statistics and probability letters , pp. 181-189.
40. Zhang, B.-T. (1994). An Incremental Learning Algorithm that Optimizes
Network Size and Sample Size in One Trial. Proceedings of International
Conference on Neural Networks (ICNN-94) , IEEE, pp. 215-220.
Search WWH ::




Custom Search