Optimal Bayesian Classification When the Training Observations are Serially Dependent

Amin Zollanvari, Edward R. Dougherty

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

Abstract

In this study, we construct the optimal Bayesian classifier (OBC) when the training observations are serially dependent. To model the effect of dependency, we assume the training observations are generated from VAR(p), which is a multi-dimensional vector autoregressive process of order p.

Original languageEnglish
Title of host publication2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538679333
DOIs
Publication statusPublished - Nov 16 2018
Event2018 New York Scientific Data Summit, NYSDS 2018 - Upton, United States
Duration: Aug 6 2018Aug 8 2018

Publication series

Name2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings

Conference

Conference2018 New York Scientific Data Summit, NYSDS 2018
CountryUnited States
CityUpton
Period8/6/188/8/18

Keywords

  • Optimal Bayesian Classification
  • Vector Autoregressive Processes

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Signal Processing
  • Modelling and Simulation

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  • Cite this

    Zollanvari, A., & Dougherty, E. R. (2018). Optimal Bayesian Classification When the Training Observations are Serially Dependent. In 2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings [8538943] (2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NYSDS.2018.8538943