| 一种改进的LSTSVM增量学习算法 |
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| 引用本文： | 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. |
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| 作者姓名： | 周水生 姚丹 |
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| 作者单位： | 西安电子科技大学 数学与统计学院, 西安 710126 |
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| 摘 要： | 基于Sherman Morrison定理和迭代算法, 提出一种改进最小二乘孪生支持向量机（SMI ILSTSVM）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.
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| 关 键 词： | 稀疏性 增量学习 最小二乘孪生支持向量机 |
| 收稿时间： | 2017-05-11 |
An Improved LSTSVM Incremental Learning Algorithm |
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| ZHOU Shuisheng,YAO Dan.An Improved LSTSVM Incremental Learning Algorithm[J].Journal of Jilin University: Sci Ed,2018,56(4):909-916. |
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| Authors: | ZHOU Shuisheng YAO Dan |
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| Institution: | School of Mathematics and Statistics, Xidian University, Xi’an 710126, China |
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| Abstract: | We proposed an improved least squares twin support vector machine (SMI ILSTSVM) incremental learning algorithm based on Sherman Morrison theorem and iterative algorithm. It solved the problem that least squares twin support vector machine (LSTSVM) did not have structural risk minimization andsparsity. The experimental results show that the proposed algorithm has high classification accuracy and high efficiency, and is suitable for noise containingcross sample set classification. |
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| Keywords: | incremental learning least squares twin support vector machine sparseness |
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