A Bayesian boosting theorem
Author:
Publisher
Elsevier BV
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software
Reference4 articles.
1. Kearns, M., Mansour, Y., 1996. On the boosting ability of top-down decision tree learning algorithms. In: Proceedings of the 28th Annual ACM Symposium on the Theory of Computing, pp. 459–468
2. Schapire, R.E., 1990. The strength of weak learnability. Machine Learning
3. Schapire, R.E., Singer, Y., 1998. Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the 11th Annual ACM Conference on Computational Learning Theory, pp. 80–91
4. Sebban, M., Nock, R., 1999. Contribution of boosting in wrapper models. In: Proceedings of the European Conference on Principles and Practice of KDD, pp. 214–222
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