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E. L. Allwein, R. E. Schapire, and Y. Singer.
Reducing multiclass to binary: A unifying approach for margin classifiers.
Journal of Machine Learning Research, 1:113-141, 2000.

R. Anand, K. G. Mehrotra, C. K. Mohan, and S. Ranka.
Efficient classification for multiclass problems using modular neural networks.
IEEE Transactions on Neural Networks, 6:117-124, 1995.

C. Angulo and A. Català.
K-SVCR. A multi-class support vector machine.
In R. López de Mántaras and E. Plaza (eds.) Proceedings of the 11th European Conference on Machine Learning (ECML-2000), pp. 31-38. Springer-Verlag, 2000.

E. Bauer and R. Kohavi.
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.
Machine Learning, 36:105-169, 1999.

S. D. Bay.
Nearest neighbor classification from multiple feature subsets.
Intelligent Data Analysis, 3(3):191-209, 1999.

C. L. Blake and C. J. Merz.
UCI repository of machine learning databases.
Department of Information and Computer Science, University of California at Irvine, Irvine CA, 1998.

L. Breiman.
Bagging predictors.
Machine Learning, 24(2):123-140, 1996.

L. Breiman, J. Friedman, R. Olshen, and C. Stone.
Classification and Regression Trees.
Wadsworth & Brooks, Pacific Grove, CA, 1984.

P. Clark and R. Boswell.
Rule induction with CN2: Some recent improvements.
In Proceedings of the 5th European Working Session on Learning (EWSL-91), pp. 151-163, Porto, Portugal, 1991. Springer-Verlag.

P. Clark and T. Niblett.
The CN2 induction algorithm.
Machine Learning, 3(4):261-283, 1989.

W. W. Cohen.
Fast effective rule induction.
In A. Prieditis and S. Russell (eds.) Proceedings of the 12th International Conference on Machine Learning (ML-95), pp. 115-123, Lake Tahoe, CA, 1995. Morgan Kaufmann.

W. W. Cohen and Y. Singer.
A simple, fast, and effective rule learner.
In Proceedings of the 16th National Conference on Artificial Intelligence (AAAI-99), pp. 335-342, Menlo Park, CA, 1999. AAAI/MIT Press.

C. Cortes and V. Vapnik.
Support-vector networks.
Machine Learning, 20(3):273-297, 1995.

T. G. Dietterich.
Machine learning research: Four current directions.
AI Magazine, 18(4):97-136, Winter 1997.

T. G. Dietterich.
Approximate statistical tests for comparing supervised classification learning algorithms.
Neural Computation, 10(7):1895-1924, 1998.

T. G. Dietterich.
Ensemble methods in machine learning.
In J. Kittler and F. Roli (eds.) First International Workshop on Multiple Classifier Systems, pp. 1-15. Springer-Verlag, 2000a.

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

T. G. Dietterich and G. Bakiri.
Solving multiclass learning problems via error-correcting output codes.
Journal of Artificial Intelligence Research, 2:263-286, 1995.

A. Feelders and W. Verkooijen.
Which method learns most from the data? Methodological issues in the analysis of comparative studies.
In Proceedings of the 5th International Workshop on Artificial Intelligence and Statistics, pp. 219-225, Fort Lauderdale, Florida, 1995.

Y. Freund and R. E. Schapire.
A decision-theoretic generalization of on-line learning and an application to boosting.
Journal of Computer and System Sciences, 55(1):119-139, 1997.

J. H. Friedman.
Another approach to polychotomous classification.
Technical report, Department of Statistics, Stanford University, Stanford, CA, 1996.

J. Fürnkranz.
Pruning algorithms for rule learning.
Machine Learning, 27(2):139-171, 1997.

J. Fürnkranz.
Exploiting structural information for text classification on the WWW.
In D. Hand, J. N. Kok, and M. Berthold (eds.) Advances in Intelligent Data Analysis: Proceedings of the 3rd International Symposium (IDA-99), pp. 487-497, Amsterdam, Netherlands, 1999a. Springer-Verlag.

J. Fürnkranz.
Separate-and-conquer rule learning.
Artificial Intelligence Review, 13(1):3-54, 1999b.

J. Fürnkranz.
Hyperlink ensembles: A case study in hypertext classification.
Technical Report OEFAI-TR-2001-30, Austrian Research Institute for Artificial Intelligence, Wien, Austria, 2001a.

J. Fürnkranz.
Round robin rule learning.
In C. E. Brodley and A. P. Danyluk (eds.) Proceedings of the 18th International Conference on Machine Learning (ICML-01), pp. 146-153, Williamstown, MA, 2001b. Morgan Kaufmann Publishers.

J. Fürnkranz and G. Widmer.
Incremental Reduced Error Pruning.
In W. Cohen and H. Hirsh (eds.) Proceedings of the 11th International Conference on Machine Learning (ML-94), pp. 70-77, New Brunswick, NJ, 1994. Morgan Kaufmann.

T. Hastie and R. Tibshirani.
Classification by pairwise coupling.
In M. I. Jordan, M. J. Kearns, and S. A. Solla (eds.) Advances in Neural Information Processing Systems 10 (NIPS-97), pp. 507-513. MIT Press, 1998.

C.-W. Hsu and C.-J. Lin.
A comparison of methods for multi-class support vector machines.
IEEE Transactions on Neural Networks, 2002.
To appear.

A. Kalousis and T. Theoharis.
Noemon: Design, implementation and performance results of an intelligent assistant for classifier selection.
Intelligent Data Analysis, 3(5):319-337, 1999.

S. Knerr, L. Personnaz, and G. Dreyfus.
Single-layer learning revisited: A stepwise procedure for building and training a neural network.
In F. Fogelman Soulié and J. Hérault (eds.) Neurocomputing: Algorithms, Architectures and Applications, volume F68 of NATO ASI Series, pp. 41-50. Springer-Verlag, 1990.

S. Knerr, L. Personnaz, and G. Dreyfus.
Handwritten digit recognition by neural networks with single-layer training.
IEEE Transactions on Neural Networks, 3(6):962-968, 1992.

J. F. Kolen and J. B. Pollack.
Back propagation is sensitive to initial conditions.
In Advances in Neural Information Processing Systems 3 (NIPS-90), pp. 860-867. Morgan Kaufmann, 1991.

U. H.-G. Kreßel.
Pairwise classification and support vector machines.
In B. Schölkopf, C. Burges, and A. Smola (eds.) Advances in Kernel Methods: Support Vector Learning, chapter 15, pp. 255-268. MIT Press, Cambridge, MA, 1999.

A. Krieger, A. J. Wyner, and C. Long.
Boosting noisy data.
In C. E. Brodley and A. P. Danyluk (eds.) Proceedings of the 18th International Conference on Machine Learning (ICML-2001), pp. 274-281. Williamstown, MA, 2001. Morgan Kaufmann Publishers.

B.-L. Lu and M. Ito.
Task decomposition and module combination based on class relations: A modular neural network for pattern classification.
IEEE Transactions on Neural Networks, 10(5):1244-1256, 1999.

E. Mayoraz and E. Alpaydin.
Support vector machines for multi-class classification.
In J. Mira and J. V. Sánchez-Andrés (eds.) Engineering Applications of Bio-Inspired Artificial Neural Networks: Proceedings of the International Work-Conference on Artificial and Natural Neural Networks (IWANN-99), Volume II, pp. 833-842, Alicante, Spain, 1999. Springer-Verlag.

E. Mayoraz and M. Moreira.
On the decomposition of polychotomies into dichotomies.
In D. H. Fisher (ed.) Proceedings of the 14th International Conference on Machine Learning (ICML-97), pp. 219-226, Nashville, TN, 1997. Morgan Kaufmann.

Q. McNemar.
Note on the sampling error of the difference between correlated proportions or percentages.
Psychometrika, 12:153-157, 1947.

M. Moreira and E. Mayoraz.
Improved pairwise coupling classification with correcting classifiers.
In C. Nédellec and C. Rouveirol (eds.) Proceedings of the 10th European Conference on Machine Learning (ECML-98), pp. 160-171, Chemnitz, Germany, 1998. Springer-Verlag.

D. Opitz and R. Maclin.
Popular ensemble methods: An empirical study.
Journal of Artificial Intelligence Research, 11:169-198, 1999.

B. Pfahringer.
Winning the KDD99 classification cup: Bagged boosting.
SIGKDD explorations, 1(2):65-66, 2000.

J. C. Platt, N. Cristianini, and J. Shawe-Taylor.
Large margin DAGs for multiclass classification.
In S. A. Solla, T. K. Leen, and K.-R. Müller (eds.) Advances in Neural Information Processing Systems 12 (NIPS-99), pp. 547-553. MIT Press, 2000.

D. Price, S. Knerr, L. Personnaz, and G. Dreyfus.
Pairwise neural network classifiers with probabilistic outputs.
In G. Tesauro, D. Touretzky, and T. Leen (eds.) Advances in Neural Information Processing Systems 7 (NIPS-94), pp. 1109-1116. MIT Press, 1995.

D. Pyle.
Data Preparation for Data Mining.
Morgan Kaufmann, San Francisco, CA, 1999.

J. R. Quinlan.
C4.5: Programs for Machine Learning.
Morgan Kaufmann, San Mateo, CA, 1993.

J. R. Quinlan.
Bagging, boosting, and C4.5.
In Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-96), pp. 725-730. AAAI/MIT Press, 1996.

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

R. E. Schapire.
Using output codes to boost multiclass learning problems.
In D. H. Fisher (ed.) Proceedings fo the 14th International Conference on Machine Learning (ICML-97), pp. 313-321, Nashville, TN, 1997. Morgan Kaufmann.

R. E. Schapire and Y. Singer.
Improved boosting algorithms using confidence-rated predictions.
Machine Learning, 37(3):297-336, 1999.

M. S. Schmidt.
Identifying speakers with support vector networks.
In Proceedings of the 28th Symposium on the Interface (INTERFACE-96), Sydney, Australia, 1996.

M. S. Schmidt and H. Gish.
Speaker identification via support vector classifiers.
In Proceedings of the 21st IEEE International Conference Conference on Acoustics, Speech, and Signal Processing (ICASSP-96), pp. 105-108,
Atlanta, GA, 1996.

G. Tesauro.
Connectionist learning of expert preferences by comparison training.
In D. Touretzky (ed.) Advances in Neural Information Processing Systems 1 (NIPS-88), pp. 99-106. Morgan Kaufmann, 1989.

P. E. Utgoff and J. Clouse.
Two kinds of training information for evaluation function learning.
In Proceedings of the 9th National Conference on Artificial Intelligence (AAAI-91), pp. 596-600, Anaheim, CA, 1991. AAAI Press.

J. Weston and C. Watkins.
Support vector machines for multi-class pattern recognition.
In M. Verleysen (ed.) Proceedings of the 7th European Symposium on Artificial Neural Networks (ESANN-99), pp. 219-224, Bruges, Belgium, 1999.

I. H. Witten and E. Frank.
Data Mining -- Practical Machine Learning Tools and Techniques with Java Implementations.
Morgan Kaufmann Publishers, 2000.

Johannes Fürnkranz 2002-03-11