Among the various methods to combine classifiers, Boosting was originally thought as an stratagem to cascade pairs of classifiers through their disagreement. I recover the same idea from the work of Niyogi et al. to show how to loosen the requirement of weak learnability, central to Boosting, and introduce a new cascading stratagem. The paper concludes with an empirical study of an implementation of the cascade that, under assumptions that mirror the conditions imposed by Viola and Jones in [VJ01], has the property to preserve the generalization ability of boosting.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/30463 |
| Date | 21 April 2004 |
| Creators | Perez-Breva, Luis |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 8 p., 8847621 bytes, 505102 bytes, application/postscript, application/pdf |
| Relation | Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory |
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