Empirical analysis of regularized multi-task learning for modelling Alzheimer’s disease progression

Wang, X., Zhou, M., Zhang, Y. et al. (3 more authors) (2024) Empirical analysis of regularized multi-task learning for modelling Alzheimer’s disease progression. In: 2023 IEEE International Conference on Bioinformatics & Biomedicine. 2023 IEEE International Conference on Bioinformatics & Biomedicine (BIBM), 05-08 Dec 2023, Istanbul and Turkey. Institute of Electrical and Electronics Engineers (IEEE) , pp. 4444-4451. ISBN 979-8-3503-3748-8

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Item Type: Proceedings Paper
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© 2023 The Authors. Except as otherwise noted, this author-accepted version of a proceedings paper published in 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: Terms—Multi-task; learning; Regression; model; Alzheimer’s disease
Dates:
  • Published: 18 January 2024
  • Published (online): 18 January 2024
  • Accepted: 1 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 Sciences Research Council
2784475
Depositing User: Symplectic Sheffield
Date Deposited: 09 Nov 2023 09:03
Last Modified: 08 Feb 2024 16:54
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: 10.1109/BIBM58861.2023.10385476
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