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Content Provider | IEEE Xplore Digital Library |
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Author | Jian Zhang Fillatre, L. Nikiforov, I. |
Copyright Year | 2015 |
Description | Author affiliation: LM2S, Univ. of Technol. of Troyes, Troyes, France (Nikiforov, I.) || Sch. of Autom., Wuhan Univ. of Technol., Wuhan, China (Jian Zhang) || I3S Lab., Univ. Nice Sophia-Antipolis, Nice, France (Fillatre, L.) |
Abstract | A Bayesian test has been previously proposed for a multiple hypothesis testing problem given the 0-1 loss function. However, this function is not suitable for many applications such as intrusion detection, anomaly detection where a quadratic loss function can be more appropriate to distinguish the concurrent hypotheses. Although a Bayesian test with the quadratic loss function has been constructed for this problem, its asymptotic performance has not yet been well studied due to its poor bounds. The main contribution of this paper is the construction of better bounds for this Bayesian test and the one associated with the 0-1 loss function. With these new bounds, it is theoretically established that the asymptotic equivalence between these two tests depends on the geometry of the parameter space associated with the hypotheses. |
Starting Page | 235 |
Ending Page | 238 |
File Size | 226336 |
Page Count | 4 |
File Format | |
e-ISBN | 9781467383127 |
DOI | 10.1109/ICIICII.2015.140 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-12-03 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Geometry Wireless sensor networks Loss function Upper bound Parameter geometry Misclassification probability Bayes methods Monitoring Signal to noise ratio Testing Asymptotic performance |
Content Type | Text |
Resource Type | Article |
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