BIB file com referências sobre combinação de classificadores e
tópicos relacionados, compilado durante o projeto.
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.
L. Breiman.
Bagging predictors.
Machine Learning, 24(2):123-140, 1996.
C. Burges.
A tutorial on support vector machines for pattern recognition.
Data Mining and Knowledge Discovery, 2:121-167, 1998.
N. Cristianini and J. Shawe-Taylor.
An Introduction to Support Vector Machines and other
kernel-based learning methods.
Cambridge University Press, 2000.
T. G. Dietterich and G. Bakiri.
Solving multiclass learning problems via error-correcting output
codes.
Artificial Intelligence Research, 1995.
X. Fan.
Efficient multiclass object detection by a hierarchy of classifiers.
In Proceedings of the 2005 IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 2005.
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, 55:119-139, 1997.
Y. Freund and R. E. Schapire.
A short introduction to boosting.
Journal of Japanese Society for Artificial Intelligence,
14(5):771-780, 1999.
in Japanese, translation by N. Abe.
N. S. T. Hirata and J. Barrera.
A unifying view for stack filter design based on graph search
methods.
Pattern Recognition, 38:2088-2098, 2005.
N. S. T. Hirata, E. R. Dougherty, and J. Barrera.
Iterative Design of Morphological Binary Image Operators.
Optical Engineering, 39(12):3106-3123, December 2000.
N. S. T. Hirata.
Binary image operator design based on stacked generalization.
In A. C. Frery and M. A. F. Rodrigues, editors, Proceedings of
the SIBGRAPI 2005, pages 63-70, 2005.
T. Hastie, R. Tibshirani, and J. Friedman.
The Elements of Statistical Learning.
Springer-Verlag, 2001.
J.-S. Lee and I.-S. Oh.
Binary classification trees for multi-class classification problems.
In Proceedings of the Seventh International Conference on
Document Analysis and Recognition, 2003.
K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf.
An introduction to kernel-based learning algorithms.
IEEE Transactions on Neural Networks, 12(2):181-201, 2001.
G. Ridgeway.
The state of boosting.
Computing Science and Statistics, 31:172-181, 1999.
R. Rifkin and A. Klautau.
In defense of one-vs-all classification.
Journal of Machine Learning Research, 5:101-141, 2004.
R. E. Schapire.
Theoretical views of boosting and applications.
In Proceedings of the 10th International Conference on
Algorithmic Learning Theory, ALT, volume 1720, pages 13-25. Springer,
1999.
D. Tsujinishi, Y. Koshiba, and S. Abe.
Why pairwise is better than one-against-all or all-at-once.
In Proc. of 2004 IEEE International Joint Conference on Neural
Networks, pages 25-29, 2004.
K. M. Ting and I. H. Witten.
Stacked generalizations: When does it work?
In IJCAI (2), pages 866-873, 1997.
K. M. Ting and I. H. Witten.
Issues in stacked generalization.
Journal of Artificial Intelligence Research, 10:271-289, 1999.
D. H. Wolpert.
Stacked generalization.
Neural Networks, 5(2):241-259, 1992.