Publication
Title
Training a hyperdimensional computing classifier using a threshold on its confidence
Author
Abstract
Hyperdimensional computing (HDC) has become popular for light-weight and energy-efficient machine learning, suitable for wearable Internet-of-Things devices and near-sensor or on-device processing. HDC is computationally less complex than traditional deep learning algorithms and achieves moderate to good classification performance. This letter proposes to extend the training procedure in HDC by taking into account not only wrongly classified samples but also samples that are correctly classified by the HDC model but with low confidence. We introduce a confidence threshold that can be tuned for each data set to achieve the best classification accuracy. The proposed training procedure is tested on UCIHAR, CTG, ISOLET, and HAND data sets for which the performance consistently improves compared to the baseline across a range of confidence threshold values. The extended training procedure also results in a shift toward higher confidence values of the correctly classified samples, making the classifier not only more accurate but also more confident about its predictions.
Language
English
Source (journal)
Neural computation. - Cambridge, Mass.
Publication
Cambridge, Mass. : 2023
ISSN
0899-7667
DOI
10.1162/NECO_A_01618
Volume/pages
35 :12 (2023) , p. 2006-2023
ISI
001125372900005
Pubmed ID
37844327
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
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Creation 05.12.2023
Last edited 02.02.2024
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