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Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem

Artificial intelligence; Computer graphics; Computer graphics equipment; Data mining; Learning systems; Parallel processing systems; Program processors; Quadratic programming; Computational time; GPU-accelerated; Graphics Processing Unit; Machine learning problem; Performance measurements; Real-time...

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Main Authors: Sulaiman M.A.H.B., Suliman A., Ahmad A.R.
Other Authors: 56727740800
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-22392
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spelling my.uniten.dspace-223922023-05-29T14:00:42Z Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem Sulaiman M.A.H.B. Suliman A. Ahmad A.R. 56727740800 25825739000 35589598800 Artificial intelligence; Computer graphics; Computer graphics equipment; Data mining; Learning systems; Parallel processing systems; Program processors; Quadratic programming; Computational time; GPU-accelerated; Graphics Processing Unit; Machine learning problem; Performance measurements; Real-time forecasting; Support vector machine (SVMs); Viable solutions; Support vector machines This paper presents performance evaluation of GPU-accelerated Support Vector Machines (SVMs) using large datasets. Although SVMs algorithm is popular among machine learning researchers and data mining practitioners, its computational time is too long and impractical for large datasets due to its complex Quadratic Programming (QP) solver. The result shows that using GPU-accelerated SVMs can significantly reduce computational time for training phase of SVMs and it can be a viable solution for any project that require real-time forecasting output. � 2014 IEEE. Final 2023-05-29T06:00:42Z 2023-05-29T06:00:42Z 2015 Conference Paper 10.1109/ICIMU.2014.7066648 2-s2.0-84937434924 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937434924&doi=10.1109%2fICIMU.2014.7066648&partnerID=40&md5=b51e49b8e1c53be511e242eb8d9a8995 https://irepository.uniten.edu.my/handle/123456789/22392 7066648 299 302 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Artificial intelligence; Computer graphics; Computer graphics equipment; Data mining; Learning systems; Parallel processing systems; Program processors; Quadratic programming; Computational time; GPU-accelerated; Graphics Processing Unit; Machine learning problem; Performance measurements; Real-time forecasting; Support vector machine (SVMs); Viable solutions; Support vector machines
author2 56727740800
author_facet 56727740800
Sulaiman M.A.H.B.
Suliman A.
Ahmad A.R.
format Conference Paper
author Sulaiman M.A.H.B.
Suliman A.
Ahmad A.R.
spellingShingle Sulaiman M.A.H.B.
Suliman A.
Ahmad A.R.
Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
author_sort Sulaiman M.A.H.B.
title Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
title_short Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
title_full Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
title_fullStr Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
title_full_unstemmed Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
title_sort measuring gpu-accelerated parallel svm performance using large datasets for multi-class machine learning problem
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806427652193517568
score 13.222552

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