10.0 Bibliography

Alpaydin, E. 2004. Introduction to machine learning. Cambridge, MA: MIT Press.

Dietterich, T. G. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems, J. Kittler and F. Roli eds. New York: Springer.

Dietterich, T. G., and G. Bakiri. 1995. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2: 263-86.

Dietrich, C., G. Palm, and F. Schewnker. 2003. Decision templates of the classification of bioacoustic time series. Information Fusion 4: 101-9.

Duin, R. P. W. 2002. The combining classifier: To train or not to train? Proceedings of the International Conference on Pattern Recognition. 765-70.

Freund, Y., and R. E. Schapire. 1996. Experiments with a new boosting algorithm. Proceedings of the International Conference on Machine Learning. 148-56.

Ho, T. K. 2002. Multiple classifier combination: Lessons and the next steps. In Hybrid Methods in Pattern Recognition, A. Kandel and H. Bunke, eds. River Edge, N.J.: World Scientific Publishing.

Jagannathan, V., R. Dodhiawala, and L.S. Baum, eds. 1989. Blackboard architectures and applications. New York: Academic Press.

Kittler, J. 2000. A framework for classifier fusion: Is it still needed? Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition. 45–56.

Kleinberg, E. M. 2000. On the algorithmic implementation of stochastic discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(5): 473-90.

Kuncheva, L. I. 2004. Combining Pattern Classifiers: Methods and Algorithms. Hoboken, N.J.: Wiley-Interscience.

Matan, O. 1996. On voting ensembles of classifiers. Proceedings of the AAAI-96 Workshop on Integrating Multiple Learned Models.84-88.

Nagy, G. Candide’s practical principles of experimental pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 5(2): 199-200.

Rastrigin, L. A., and R. H. Erenstein. 1981. Method of collective recognition. Moscow: Energoizdat.

Schapire, R. E. 1990. The strength of weak learnability. Machine Learning 5: 197-227.

Woods, K., W. P. Kegelmeyer, and K. Bowyer. 1997. Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19: 405-10.

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