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

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dc.citedby2
dc.contributor.authorSulaiman M.A.H.B.en_US
dc.contributor.authorSuliman A.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorid56727740800en_US
dc.contributor.authorid25825739000en_US
dc.contributor.authorid35589598800en_US
dc.date.accessioned2023-05-29T06:00:42Z
dc.date.available2023-05-29T06:00:42Z
dc.date.issued2015
dc.descriptionArtificial 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 machinesen_US
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7066648
dc.identifier.doi10.1109/ICIMU.2014.7066648
dc.identifier.epage302
dc.identifier.scopus2-s2.0-84937434924
dc.identifier.spage299
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84937434924&doi=10.1109%2fICIMU.2014.7066648&partnerID=40&md5=b51e49b8e1c53be511e242eb8d9a8995
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22392
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleConference Proceedings - 6th International Conference on Information Technology and Multimedia at UNITEN: Cultivating Creativity and Enabling Technology Through the Internet of Things, ICIMU 2014
dc.titleMeasuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problemen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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