Publication: Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
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Date
2015
Authors
Sulaiman M.A.H.B.
Suliman A.
Ahmad A.R.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
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.
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