Bibliography

Bauer Kohavi1999

Bauer, E. Kohavi, R. 1999.
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
Machine Learning, 36, 105-139.

Blake et al.1998

Blake, C. L., Keogh, E., Merz, C. 1998.
UCI repository of machine learning databases.
http://www.ics.uci.edu/$\sim$mlearn/MLRepository.html.

Breiman1996

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

Breiman et al.1984

Breiman, L., Freidman, J. H., Olshen, R. A., Stone, C. J. 1984.
Classification and Regression Trees.
Wadsworth.

Buja Lee2001

Buja, A. Lee, Y.-S. 2001.
Data mining criteria for tree-based regression and classification
In Proceedings of the 7$^{th}$ International Conference on Knowledge Discovery in Databases, 27-36.

Buntine Niblett1992

Buntine, W. Niblett, T. 1992.
A further comparison of splitting rules for Decision-Tree induction
Machine Learning, 8, 75-85.

Clark Boswell1991

Clark, P. Boswell, R. 1991.
Rule induction with CN2: some recent improvements
In Proceedings of the 6$^{~th}$ European Working Session in Learning, 155-161.

Cohen Singer1999

Cohen, W. W. Singer, Y. 1999.
A Simple, Fast and Effective Rule Learner
In Proceedings of the 16$^{th}$ National Conference on Artificial Intelligence, 335-342.

de Carvalho Gomes Gascuel1994

de Carvalho Gomes, F. A. Gascuel, O. 1994.
SDL, a stochastic algorithm for learning decision lists with limited complexity
Annals of Mathematics and Artificial Intelligence, 10, 281-302.

Dietterich2000

Dietterich, T. G. 2000.
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
Machine Learning, 40, 139-157.

Domingos1998

Domingos, P. 1998.
A Process-oriented Heuristic for Model selection
In Proceedings of the 15$^{~th}$ International Conference on Machine Learning, 127-135.

Feige1996

Feige, U. 1996.
A threshold of ln $n$ for approximating set cover
In Proceedings of the 28$^{~th}$ ACM Symposium on the Theory of Computing, 314-318.

Franck Witten1998

Franck, E. Witten, I. 1998.
Using a Permutation Test for Attribute selection in Decision Trees
In Proceedings of the 15$^{~th}$ International Conference on Machine Learning, 152-160.

Freund Mason1999

Freund, Y. Mason, L. 1999.
The alternating decision tree learning algorithm
In Proceedings of the 16$^{~th}$ International Conference on Machine Learning, 124-133.

Friedman et al.2000

Friedman, J., Hastie, T., Tibshirani, R. 2000.
Additive Logistic Regression : a Statistical View of Boosting
Annals of Statistics, 28, 337-374.

Garey Johnson1979

Garey, M. Johnson, D. 1979.
Computers and Intractability, a guide to the theory of NP-Completeness.
Bell Telephone Laboratories.

Grötschel et al.1981

Grötschel, M., Lovàsz, L., Schrijver, A. 1981.
The ellipsoid method and its consequences in combinatorial optimization
Combinatorica, 1, 169-197.

Holte1993

Holte, R. 1993.
Very simple classification rules perform well on most commonly used datasets
Machine Learning, 11, 63-91.

Hyafil Rivest1976

Hyafil, L. Rivest, R. 1976.
Constructing optimal decision trees is NP-complete
Information Processing Letters, 5, 15-17.

John et al.1994

John, G. H., Kohavi, R., Pfleger, K. 1994.
Irrelevant features and the subset selection problem
In Proceedings of the 11$^{~th}$ International Conference on Machine Learning, 121-129.

Kearns Mansour1998

Kearns, M. J. Mansour, Y. 1998.
A Fast, Bottom-up Decision Tree Pruning algorithm with Near-Optimal generalization
In Proceedings of the 15$^{~th}$ International Conference on Machine Learning, 269-277.

Kearns et al.1987

Kearns, M., Li, M., Pitt, L., Valiant, L. 1987.
On the learnability of boolean formulae
In Proceedings of the 19$^{~th}$ ACM Symposium on the Theory of Computing, 285-295.

Kohavi Sommerfield1998

Kohavi, D. Sommerfield, D. 1998.
Targetting Business users with Decision Table Classifiers
In Proceedings of the 4$^{th}$ International Conference on Knowledge Discovery in Databases, 249-253.

Mansour McAllester2000

Mansour, Y. McAllester, D. 2000.
Boosting using branching programs
In Proceedings of the 13$^{~th}$ International Conference on Computational Learning Theory, 220-224.

Margineantu Dietterich1997

Margineantu, D. Dietterich, T. G. 1997.
Pruning adaptive boosting
In Proceedings of the 14$^{~th}$ International Conference on Machine Learning, 211-218.

Mitchell1997

Mitchell, T. 1997.
Machine Learning.
McGraw-Hill.

Nock Gascuel1995

Nock, R. Gascuel, O. 1995.
On learning decision committees
In Proceedings of the 12$^{~th}$ International Conference on Machine Learning, 413-420. Morgan Kaufmann.

Nock Jappy1998

Nock, R. Jappy, P. 1998.
On the power of decision lists
In Proceedings of the 15$^{~th}$ International Conference on Machine Learning, 413-420. Morgan Kaufmann.

Opitz Maclin1999

Opitz, D. Maclin, R. 1999.
Popular ensemble methods: a survey
Journal of Artificial Intelligence Research, 11, 169-198.

Queyranne1998

Queyranne, M. 1998.
Minimizing symmetric submodular functions
Mathematical Programming, 82, 3-12.

Quinlan1994

Quinlan, J. R. 1994.
C4.5 : programs for machine learning.
Morgan Kaufmann.

Quinlan1996

Quinlan, J. R. 1996.
Bagging, Boosting and C4.5
In Proceedings of the 13$^{th}$ National Conference on Artificial Intelligence, 725-730.

Ridgeway et al.1998

Ridgeway, G., Madigan, D., Richardson, T., O'Kane, J. 1998.
Interpretable boosted naive bayes classification
In Proceedings of the 4$^{th}$ International Conference on Knowledge Discovery in Databases, 101-104.

Rivest1987

Rivest, R. 1987.
Learning decision lists
Machine Learning, 2, 229-246.

Schapire et al.1998

Schapire, R. E., Freund, Y., Bartlett, P., Lee, W. S. 1998.
Boosting the Margin : a new explanation for the effectiveness of Voting methods
Annals of statistics, 26, 1651-1686.

Schapire Singer1998

Schapire, R. E. Singer, Y. 1998.
Improved boosting algorithms using confidence-rated predictions
In Proceedings of the 11$^{~th}$ International Conference on Computational Learning Theory, 80-91.

Valiant1984

Valiant, L. G. 1984.
A theory of the learnable
Communications of the ACM, 27, 1134-1142.

Valiant1985

Valiant, L. G. 1985.
Learning disjunctions of conjunctions
In Proceedings of the 9$^{~th}$ International Joint Conference on Artificial Intelligence, 560-566.