Efficient support vector machine training algorithm on GPUs

Jiashuai Shi, Zeyi Wen, Bingsheng He, Jian Chen

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

Support Vector Machines (SVMs) are popular for many machine learning tasks. With rapid growth of dataset size, the high cost of training limits the wide use of SVMs. Several SVM implementations on GPUs have been proposed to accelerate SVMs. However, they support only classification (SVC) or regression (SVR). In this work, we propose a simple and effective SVM training algorithm on GPUs which can be used for SVC, SVR and one-class SVM. Initial experiments show that our implementation outperforms existing ones. We are in the process of encapsulating our algorithm into an easy-to-use library which has Python, R and MATLAB interfaces.

Original languageEnglish
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Place of PublicationLouisiana USA
PublisherAAAI Press
Pages8157-8158
Number of pages2
ISBN (Electronic)9781577358008
Publication statusPublished - 2018
Externally publishedYes
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: 2 Feb 20187 Feb 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Conference

Conference32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/02/187/02/18

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