Nonparametric training algorithm for decentralized binary hypothesis testing networks

John Wissinger, Michael Athans

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a distributed nonparametric minimum-error training algorithm for networks of linear threshold classifiers performing decentralized binary hypothesis testing (detection). The training algorithm consists of communicating stochastic approximation algorithms. Knowledge of the network topology is required by the algorithm. We suggest that models of the variety in this study provide a paradigm for the study of adaptation in human decision making organizations.

Original languageEnglish (US)
Title of host publicationAmerican Control Conference
Editors Anon
PublisherPubl by IEEE
Pages176-177
Number of pages2
ISBN (Print)0780308611
StatePublished - Dec 1 1993
EventProceedings of the 1993 American Control Conference Part 3 (of 3) - San Francisco, CA, USA
Duration: Jun 2 1993Jun 4 1993

Publication series

NameAmerican Control Conference

Other

OtherProceedings of the 1993 American Control Conference Part 3 (of 3)
CitySan Francisco, CA, USA
Period6/2/936/4/93

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Wissinger, J., & Athans, M. (1993). Nonparametric training algorithm for decentralized binary hypothesis testing networks. In Anon (Ed.), American Control Conference (pp. 176-177). (American Control Conference). Publ by IEEE.