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Optimizing Boosting with Discriminative Criteria
Computer Science Department
  • Rong Zhang, Carnegie Mellon University
  • Alexander I Rudnicky, Carnegie Mellon University
Date of Original Version
Conference Proceeding
Abstract or Description

We describe the use of discriminative criteria to optimize Boosting based ensembles. Boosting algorithms may create hundreds of individual classifiers in order to fit the training data. However, this strategy isn’t feasible and necessary for complex classification problems, such as real-time continuous speech recognition, in which only the combination of a few of acoustic models is practical. How to improve the classification accuracy for small size of ensemble is the focus of this paper. Two discriminative criteria that attempt to minimize the true Bayes error rate are investigated. Improvements are observed over a variety of datasets including image and speech recognition, indicating the prospective utility of these two criteria.

Citation Information
Rong Zhang and Alexander I Rudnicky. "Optimizing Boosting with Discriminative Criteria" (2004)
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