Uncertainty in Artificial Intelligence
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Geometric Implications of the Naive Bayes Assumption
Mark Peot
Abstract:
A naive (or Idiot) Bayes network is a network with a single hypothesis node and several observations that are conditionally independent given the hypothesis. We recently surveyed a number of members of the UAI community and discovered a general lack of understanding of the implications of the Naive Bayes assumption on the kinds of problems that can be solved by these networks. It has long been recognized [Minsky 61] that if observations are binary, the decision surfaces in these networks are hyperplanes. We extend this result (hyperplane separability) to Naive Bayes networks with m-ary observations. In addition, we illustrate the effect of observation-observation dependencies on decision surfaces. Finally, we discuss the implications of these results on knowledge acquisition and research in learning.
Keywords:
Pages: 414-419
PS Link:
PDF Link: /papers/96/p414-peot.pdf
BibTex:
@INPROCEEDINGS{Peot96,
AUTHOR = "Mark Peot ",
TITLE = "Geometric Implications of the Naive Bayes Assumption",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "414--419"
}


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