Uncertainty in Artificial Intelligence
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Budgeted Learning of Naive-Bayes Classifiers
Daniel Lizotte, Omid Madani, Russell Greiner
Abstract:
Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT exceed the budget.This paper compares methods FOR choosing which feature label TO purchase next, given the budget AND the CURRENT belief state OF naive Bayes model parameters.Whereas active learning has traditionally focused ON myopic(greedy) strategies FOR query selection, this paper presents a tractable method FOR incorporating knowledge OF the budget INTO the decision making process, which improves performance
Keywords:
Pages: 378-385
PS Link:
PDF Link: /papers/03/p378-lizotte.pdf
BibTex:
@INPROCEEDINGS{Lizotte03,
AUTHOR = "Daniel Lizotte and Omid Madani and Russell Greiner",
TITLE = "Budgeted Learning of Naive-Bayes Classifiers",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "378--385"
}


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