01/11/05:

• Class Survey
• From Data Mining to Knowledge Discovery in Databases, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth
• How Much Information? 2003

01/13/05

• Does Machine Learning Really work? by Tom M. Mitchell, Published by American Association of Artificial Intelligence

01/26/05

• Bruha. From machine learning to knowledge discovery: Survey of preprocessing and postprocessing
• R. C. Holte. Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning, vol. 3, pp. 63-91, 1993

02/08/05

• Fast Effective Rule Induction by William W. Cohen, Published by Machine Learning: Proceedings of the Twelfth International Confernece
• Is Error-Based Pruning Redeemable? by Lawrence O. Hall, Published by International Journal of Artificial Intelligence

02/16/05

• Robust Classification for Impercise Enviroments by Foster Provost

02/21/05

• Theoretical and Emperical Analysis of ReliefF and RReliefF by Robnik, Published by Machine Learning Journal 2003
• Improved Heterogeneous Distance Functions by Wilson, Published by Journal of Artificial Intelligence Research, 1997

03/14/05

• Data Clustering: A Review by A. K. Jain, M.N. Murty, P.J. Flynn

03/26/05

• Learning ensembles from bites: A scalable and accurate approach by Chawla, N.V., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P., Journal of Machine Learning Research, 5 (2004), 421 -- 451.

• Distributed Learning with Bagging like Performance by Chawla, N.V., Moore, T.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P., Springer C., Pattern Recognition Letters, 24 (1-3) (2003), 455 -- 471.

• Ensemble Methods in Machine Learning by Dietterich, T. G., In J.Kittler and F. Roli (Ed.) First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science (pp. 1-15). New York: Springer Verlag. Postscript preprint Springer-Verlag, 2000.

• An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization by Dietterich, T. G., Machine Learning, 40 (2) 139-158, 2000.

• The Random Subspace Method for Constructing Decision Forests by Ho, T. K., IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 , 8, August 1998, 832-844.

• Random Forests by Breiman, L., Machine Learning, 45, 5-32 (2001).

• Experiments with a new boosting algorithm by Freund, Y. and Schapire, R.E., In Proc. 13th International Conference on Machine Learning, pages 148-146. Morgan Kaufmann, 1996.

• Bagging predictors by Breiman, L., Machine Learning 24 123-140, 1996.

• Bagging, boosting, and C4.5 by Quinlan, J.R., In Proceedings of the Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference, pages 725-730, Menlo Park, August4-8 1996. AAAI Press / MIT Press.