An Improved Learning algorithm for Branch and Bound for Semi-Supervised Support Vector Machines

ZHAO Ying; ZHANG Jian-pei; YANG Jing; WANG Guan-jun

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An Improved Learning algorithm for Branch and Bound for Semi-Supervised Support Vector Machines

更新时间：2025-07-16

- An Improved Learning algorithm for Branch and Bound for Semi-Supervised Support Vector Machines
- Acta Electronica Sinica Vol. 38, Issue 2, Pages: 449-454(2010)
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- Published：2010
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ZHAO Ying, ZHANG Jian-pei, YANG Jing, et al. An Improved Learning algorithm for Branch and Bound for Semi-Supervised Support Vector Machines[J]. Acta Electronica Sinica, 2010, 38(2): 449-454.
DOI：

ZHAO Ying, ZHANG Jian-pei, YANG Jing, et al. An Improved Learning algorithm for Branch and Bound for Semi-Supervised Support Vector Machines[J]. Acta Electronica Sinica, 2010, 38(2): 449-454. DOI：

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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