Large scale multi-output multi-class classification using Gaussian processes

Ma, C. orcid.org/0000-0002-8534-4720 and Álvarez, M.A. (2023) Large scale multi-output multi-class classification using Gaussian processes. Machine Learning, 112. pp. 1077-1106. ISSN 0885-6125

Abstract

Metadata

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

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords: Gaussian processes; Multi-output Gaussian processes; Image data; Classification; Transfer learning
Dates:
  • Published: April 2023
  • Published (online): 8 February 2023
  • Accepted: 24 November 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
Funder
Grant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/R034303/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/V029045/1
Depositing User: Symplectic Sheffield
Date Deposited: 03 Apr 2023 11:27
Last Modified: 03 Apr 2023 11:27
Published Version: http://dx.doi.org/10.1007/s10994-022-06289-3
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: 10.1007/s10994-022-06289-3
Open Archives Initiative ID (OAI ID):

Export

Statistics