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Title: Robust Decentralized Detection and Social Learning in Tandem Networks
Authors: Ho, Jack
Tay, Wee Peng
Quek, Tony Q. S.
Chong, Edwin K. P.
Keywords: Social learning
Decentralized detection
Issue Date: 2015
Source: Ho, J., Tay, W. P., Quek, T. Q. S., & Chong, E. K. P. (2015). Robust Decentralized Detection and Social Learning in Tandem Networks. IEEE Transactions on Signal Processing, 63(19), 5019-5032.
Series/Report no.: IEEE Transactions on Signal Processing
Abstract: We study a tandem of agents who make decisions about an underlying binary hypothesis, where the distribution of the agent observations under each hypothesis comes from an uncertainty class defined by a 2-alternating capacity. We investigate both decentralized detection rules, where agents collaborate to minimize the error probability of the final agent, and social learning rules, where each agent minimizes its own local minimax error probability. We then extend our results to the infinite tandem network, and derive necessary and sufficient conditions on the uncertainty classes for the minimax error probability to converge to zero when agents know their positions in the tandem. On the other hand, when agents do not know their positions in the network, we study the cases where agents collaborate to minimize the asymptotic minimax error probability, and where agents seek to minimize their worst-case minimax error probability (over all possible positions in the tandem). We show that asymptotic learning of the true hypothesis is no longer possible in these cases, and derive characterizations for the minimax error performance.
ISSN: 1053-587X
DOI: 10.1109/TSP.2015.2448525
Schools: School of Electrical and Electronic Engineering 
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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