吉林大学学报(理学版)
• 计算机科学 • 上一篇 下一篇
周水生, 姚丹
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ZHOU Shuisheng, YAO Dan
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摘要： 基于ShermanMorrison定理和迭代算法, 提出一种改进最小二乘孪生支持向量机（SMIILSTSVM）的增量学习算法, 解决了最小二乘孪生支持向量机（LSTSVM）不具备结构风险最小化和稀疏性的问题. 实验结果表明, 该算法分类精度和效率均较高, 适用于含有噪声的交叉样本集分类.
关键词: 稀疏性, 增量学习, 最小二乘孪生支持向量机
Abstract: We proposed an improved least squares twin support vector machine (SMIILSTSVM) incremental learning algorithm based on ShermanMorrison theorem and iterative algorithm. It solved the problem that least squares twin support vector machine (LSTSVM) did not have structural risk minimization and sparsity. The experimental results show that the proposed algorithm has high classification accuracy and high efficiency, and is suitable for noisecontaining crosssample set classification.
Key words: incremental learning, least squares twin support vector machine, sparseness
中图分类号:
周水生, 姚丹. 一种改进的LSTSVM增量学习算法[J]. 吉林大学学报(理学版), 2018, 56(4): 909-916.
ZHOU Shuisheng, YAO Dan. An Improved LSTSVM Incremental Learning Algorithm[J]. Journal of Jilin University Science Edition, 2018, 56(4): 909-916.
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链接本文: http://xuebao.jlu.edu.cn/lxb/CN/
http://xuebao.jlu.edu.cn/lxb/CN/Y2018/V56/I4/909
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