Two-sided exponential concentration bounds for Bayes error rate and Shannon entropy
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Jaakkola_Two-sided exponential.pdf
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Author(s)
Honorio, Jean
Jaakkola, Tommi S.
Date Issued
2013
Journal
Journal of Machine Learning Research
Publisher
Association for Computing Machinery (ACM)
Citation
Honorio, Jean, and Tommi Jaakkola. "Two-sided exponential concentration bounds for Bayes error rate and Shannon entropy." Journal of Machine Learning Research W&CP 28(3): 459–467 (2013).
Version
Final published version
Abstract
We provide a method that approximates the Bayes error rate and the Shannon entropy with high probability. The Bayes error rate approximation makes possible to build a classifier that polynomially approaches Bayes error rate. The Shannon entropy approximation provides provable performance guarantees for learning trees and Bayesian networks from continuous variables. Our results rely on some reasonable regularity conditions of the unknown probability distributions, and apply to bounded as well as unbounded variables.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://jmlr.org/proceedings/papers/v28/honorio13.html
