计算机工程与应用Issue(12)：111-117,188,8.DOI:10.3778/j.issn.1002-8331.1409-0194
GGMC算法目标函数值实验分析与算法改进
Experimental analysis on object function value of GGMC algorithm and algorithm improvement
摘要
Abstract
Aiming at the problem of high computational complexity of Greed Max-Cut Graph semi-supervised learning algorithm(GGMC), an improved Greed Max-Cut Graph semi-supervised learning algorithm based on Early stopping strategy, called GGMC-Estop, is proposed. According to the experimental analysis on that object function value in optimi-zation procedure of GGMC, the algorithm is improved in which two early stopping strategies are applied to stop GGMC training and prediction. Standard propagation is used to predict the label of data over the whole graph in one step. Experi-mental results on typical data sets show that the computational amount using the improved algorithm is far less than that of using GGMC algorithm, while the performance of classification for these two algorithms is almost approximate.关键词
图半监督学习/贪心最大割/早期停止策略/目标函数值Key words
graph semi-supervised learning/greedy max-cut/early stopping strategy/object function value分类
信息技术与安全科学引用本文复制引用
修宇,王骏,王忠群,皇苏斌..GGMC算法目标函数值实验分析与算法改进[J].计算机工程与应用,2015,(12):111-117,188,8.基金项目
国家自然科学基金（No.71371012，No.71171002）；教育部人文社科规划项目（No.13YJA630098）；安徽省优秀青年人才基金重点项目（No.2013SQRL034Z）；安徽省高校省级科学研究项目（No.TSKJ2014B10）；安徽工程大学青年基金项目（No.2013YQ30）。 （）
