Scheduling IDK classifiers with arbitrary dependences to minimize the expected time to successful classification

Abdelzaher, Tarek F., Agrawa, Kunal, Baruah, Sanjoy et al. (4 more authors) (2023) Scheduling IDK classifiers with arbitrary dependences to minimize the expected time to successful classification. Real-Time Systems. ISSN 1573-1383

Abstract

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© The Author(s) 2023

Keywords: Real-time,Arbitrary dependences,DNN,Classifiers,Optimal ordering
Dates:
  • Published (online): 13 March 2023
  • Accepted: 16 February 2023
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Computer Science (York)
Funding Information:
Funder
Grant number
INNOVATE UK
113213/SUP-00007484
Depositing User: Pure (York)
Date Deposited: 30 Jun 2023 08:20
Last Modified: 21 Jan 2025 18:09
Published Version: https://doi.org/10.1007/s11241-023-09395-0
Status: Published online
Refereed: Yes
Identification Number: 10.1007/s11241-023-09395-0
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Filename: s11241_023_09395_0.pdf

Description: Scheduling IDK classifers with arbitrary dependences to minimize the expected time to successful classifcation

Licence: CC-BY 2.5

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