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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Institute of Electrical and Electronics Engineers Inc.
2023
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| author | Sulaiman M.A.H.B. Suliman A. Ahmad A.R. |
| author2 | 56727740800 |
| author_facet | 56727740800 Sulaiman M.A.H.B. Suliman A. Ahmad A.R. |
| author_sort | Sulaiman M.A.H.B. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| format | Conference Paper |
| id | my.uniten.dspace-22392 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| 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 |
| 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 |
| title | 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_short | 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 |
| url_provider | http://dspace.uniten.edu.my/ |