A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A local mean-based nonparametric classifier
Pattern Recognition Letters
Resampling or Reweighting: A Comparison of Boosting Implementations
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
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We study boosting by using a gating mechanism, Gated Boosting, to perform resampling instead of the weighting mechanism used in Adaboost. In our method, gating networks determine the distribution of the samples for training a consecutive base classifier, considering the predictions of the prior base classifiers. Using gating networks prevents the training instances from being repeatedly included in different subsets used for training base classifiers, being a key goal in achieving diversity. Furthermore, these are the gating networks that determine which classifiers' output to be pooled for producing the final output. The performance of the proposed method is demonstrated and compared to Adaboost on four benchmarks from the UCI repository, and MNIST dataset.