@inproceedings{3b80c8af9a17411a8b78f56ae3b8e91e,
title = "A GPU Based SVM Method with Accelerated Kernel Matrix Calculation",
abstract = "Support vector machine (SVM) is a popular classifier dealing with small-scale datasets. It has outstanding performance compared to other classifiers. However the execution time is extremely long when training Big Data. The Graphics Processing Unit (GPU) is a massively parallel device which performs very well as a co-processor. NVIDIA proposed a programming platform, CUDA, in 2006, which makes it much easier to program on GPU for large-scale calculation. This paper proposes a GPU based accelerated SVM method. Matrix multiplication, which is the main procedure of kernel matrix calculation in the training stage, is the major issue for high time complexity. Thus, we propose a method to calculate the kernel matrix using GPU. The polynomial kernel function is applied in this paper. Experiment results indicate that the proposed method can increase the computation speed by as much as 41 times for the studied datasets.",
keywords = "Big Data, CUDA, cross validation, graphics processing unit, kernel matrix, support vector machine",
author = "Bo Yan and Yitian Ren and Zijiang Yang",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 4th IEEE International Congress on Big Data, BigData Congress 2015 ; Conference date: 27-06-2015 Through 02-07-2015",
year = "2015",
month = aug,
day = "17",
doi = "10.1109/BigDataCongress.2015.16",
language = "English",
series = "Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "41--46",
editor = "Carminati Barbara and Latifur Khan",
booktitle = "Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015",
address = "United States",
}