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Role of machine learning in configuration management of ad hoc wireless networks

Published: 22 August 2005 Publication History

Abstract

In this work, we show that machine learning, e.g., graphical models, plays an important role for the self-configuration of ad hoc wireless network. The role of such a learning approach includes a simple representation of complex dependencies in the network and a distributed algorithm which can adaptively find a nearly optimal configuration.

References

[1]
S. Geman, and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Trans. PAMI vol. 6, pp. 721--741, 1984
[2]
K. Huang, "Statistical Mechanics," John Wiley & Sons
  1. Role of machine learning in configuration management of ad hoc wireless networks

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    cover image ACM Conferences
    MineNet '05: Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
    August 2005
    296 pages
    ISBN:1595930264
    DOI:10.1145/1080173
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 22 August 2005

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    SIGCOMM05: ACM SIGCOMM 2005 Conference
    August 26, 2005
    Pennsylvania, Philadelphia, USA

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