Bibliography - Data Mining with Decision Trees: Theory and Applications - page 293

Pfahringer B., Compression-based feature subset selection, In Proceeding of
the IJCAI- 95 Workshop on Data Engineering for Inductive Learning ,
pp. 109-119, 1995.
Pfahringer B., Bensusan H., and Giraud-Carrier C., Tell me who can learn you
and i can tell you who you are: Landmarking various learning algorithms,
In Proc. of the Seventeenth International Conference on Machine Learning
(ICML2000), pp. 743-750, 2000.
Piatetsky-Shapiro G., Discovery Analysis and Presentation of Strong Rules ,
Knowledge Discovery in Databases, Portland, OR: AAAI/MIT Press, 1991.
Poggio T., and Girosi F., Networks for approximation and learning, Proc. IEER ,
78(9):1481-1496, 1990.
Polikar R., Ensemble based systems in decision making, IEEE Circuits and
Systems Magazine 6(3):21-45, 2006.
Pratt L. Y., Mostow J., and Kamm C. A., Direct transfer of learned information
among neural networks, In Proceedings of the Ninth National Conference
on Artificial Intelligence , Anaheim, CA, pp. 584-589, 1991.
Prodromidis A. L., Stolfo S. J., and Chan P. K., Effective and ecient pruning
of metaclassifiers in a distributed data mining system, Technical report
CUCS-017-99, Columbia Univ., 1999.
Provan G. M., and Singh M., Learning Bayesian networks using feature selection,
In Learning from Data, Lecture Notes in Statistics , D. Fisher and H. Lenz
(eds.), pp. 291-300. New York: Springer-Verlag, 1996.
Provost F., Goal-Directed inductive learning: Trading off accuracy for reduced
error cost, AAAI Spring Symposium on Goal-Driven Learning, 1994.
Provost F., and Fawcett T., Analysis and visualization of Classifier Performance
Comparison under Imprecise Class and Cost Distribution, In Proceedings
of KDD-97 , pp. 43-48, AAAI Press, 1997.
Provost F., and Fawcett T., The case against accuracy estimation for comparing
induction algorithms, In Proc. 15th Intl. Conf. On Machine Learning ,
pp. 445-453, Madison, WI, 1998.
Provost F., and Fawcett T., Robust { C } lassification for { I } mprecise

{ E } nvironments, Machine Learning 42(3):203-231, 2001.
Provost F. J., and Kolluri V., A survey of methods for scaling up inductive
learning algorithms, Proc. 3rd International Conference on Knowledge
Discovery and Data Mining , 1997.
Provost F., Jensen D., and Oates T., Ecient progressive sampling, In Proceedings
of the Fifth International Conference on Knowledge Discovery and Data
Mining , pp. 23-32, 1999.
Quinlan J. R., Learning ecient classification procedures and their application
to chess endgames, Machine Learning: An AI Approach , R. Michalski,
J. Carbonell, and T. Mitchel (eds.), Los Altos, CA: Morgan Kaufman, 1983.
Quinlan J. R., Induction of decision trees, Machine Learning 1:81-106, 1986.
Quinlan J. R., Simplifying decision trees, International Journal of Man-Machine
Studies 27:221-234, 1987.