TY - GEN
T1 - Efficient support vector machine training algorithm on GPUs
AU - Shi, Jiashuai
AU - Wen, Zeyi
AU - He, Bingsheng
AU - Chen, Jian
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060470024&partnerID=8YFLogxK
UR - https://aaai.org/Library/AAAI/aaai18contents.php
M3 - Conference paper
AN - SCOPUS:85060470024
T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
SP - 8157
EP - 8158
BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PB - AAAI Press
CY - Louisiana USA
T2 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Y2 - 2 February 2018 through 7 February 2018
ER -