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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Institution:	Universiti Tenaga Nasional

id	my.uniten.dspace-22392
record_format	dspace
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

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

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