Parallel SMO Algorithm Implementation Based on OpenMP

被引：0
|
作者：
Chang, Pengfei [1 ]
Bi, Zhuo [1 ]
Feng, Yiyong [1 ]
机构：
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEM SCIENCE AND ENGINEERING (ICSSE) | 2014年
关键词：
sequential minimal optimization (SMO); support vector machine (SVM); parallel algorithm; OpenMP; SUPPORT VECTOR MACHINES;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Sequential minimal optimization (SMO) algorithm is widely used for solving the optimization problem during the training process of support vector machine (SVM). However, the SMO algorithm is quite time-consuming when handling very large training sets and thus limits the performance of SVM. In this paper, a parallel implementation of SMO algorithm is designed with OpenMP, basing on the running time analysis of each function in SMO. Experimental results show that the performance for training SVM had been improved with parallel SMO when dealing with large datasets.
引用
收藏
页码：236 / 240
页数：5
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