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An analysis of Bayesian classifiers (1992)
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| Venue: | IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE |
| Citations: | 439 - 17 self |
BibTeX
@INPROCEEDINGS{Langley92ananalysis,
author = {Pat Langley and Wayne Iba and Kevin Thompson},
title = {An analysis of Bayesian classifiers},
booktitle = {IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE},
year = {1992},
pages = {223--228},
publisher = {MIT Press}
}
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Abstract
In this paper we present anaverage-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level of class noise. We also explore the behavioral implications of the analysis by presenting
