| 一种改进的分枝定界半监督支持向量机学习算法 |
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| 引用本文： | 赵莹,张健沛,杨静,王冠军. 一种改进的分枝定界半监督支持向量机学习算法[J]. 电子学报, 2010, 38(2): 449-454 |
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| 作者姓名： | 赵莹 张健沛 杨静 王冠军 |
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| 作者单位： | 哈尔滨工程大学计算机科学与技术学院,黑龙江,哈尔滨,150001;中国矿业大学计算机学院,江苏,徐州,221116 |
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| 基金项目： | 国家自然科学基金(No.60873037,No.60673131) |
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| 摘 要： | 分枝定界半监督支持向量机，由于其实现的是全局最优化，因而可以作为其它半监督学习算法的一个基准。针对分枝定界半监督支持向量机中存在的缺陷，提出一种改进的分枝定界半监督支持向量机学习算法。该算法重新对下界的估计进行定义，从而降低了各结点计算下界的时间复杂度；同时利用支持向量机的几何特点确定分枝结点，以提高算法的运算速度。实验分析表明本文提出的算法具有精度高、鲁棒性强等优点。
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| 关 键 词： | 半监督学习 支持向量机 分枝定界 统计学习理论 |
| 收稿时间： | 2009-03-20 |
| 修稿时间： | 2009-07-01 |
An Improved Learning Algorithm for Branch and Bound for Semi-Supervised Support Vector Machines |
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| ZHAO Ying,ZHANG Jian-pei,YANG Jing,WANG Guan-jun. An Improved Learning Algorithm for Branch and Bound for Semi-Supervised Support Vector Machines[J]. Acta Electronica Sinica, 2010, 38(2): 449-454 |
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| Authors: | ZHAO Ying ZHANG Jian-pei YANG Jing WANG Guan-jun |
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| Affiliation: | 1. College of Computer Science and Technology , Harbin Engineering University, Harbin,Heilongjiang 150001,China)(2. College of Computer Science and Technology ,China university of mining and technology, XuZhou,Jiangsu 221116,China |
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| Abstract: | Branch and bound semi-supervised support vector machines as an exact globally optimization is useful for benchmarking practical semi-supervised support vector machines implementations.An improved learning algorithm for branch and bound for semi-supervised support vector machines is presented,concerning the defects of the branch and bound for semi-supervised support vector machines.The estimations of the node lower bound are redefined,which can reduce time complexity of computing the lower bound on every nod... |
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| Keywords: | semi-supervised learning support vector machines branch and bound statistic theory |
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