An incremental learning algorithm for improved least squares twin support vector machine

机译：改进的最小二乘孪生支持向量机的增量学习算法

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In this paper, we mainly propose an incremental version of improved least squares twin support vector machine (IILSTSVM), based on inverse matrix-free method. This algorithm can meet the requirement of online learning to update the existing model. In the case of low dimension data, this method effectively improves training speed of incremental learning. According to updating inverse matrix, we can implement the incremental learning for ILSTSVM. Experiments prove that this algorithm has excellent performance on runtime and recognition rate in the low dimensional space.

机译：在本文中，我们主要基于无矩阵逆方法提出一种改进的最小二乘孪生支持向量机（IILSTSVM）的增量版本。该算法可以满足在线学习更新现有模型的要求。在低维数据的情况下，该方法有效地提高了增量学习的训练速度。根据更新的逆矩阵，我们可以实现ILSTSVM的增量学习。实验证明，该算法在低维空间中具有良好的运行时间和识别率。

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来源
《2012 IEEE Fifth International Conference on Advanced Computational Intelligence.》|2012年|p.463-467|共5页
会议地点 Nanjing(CN);Nanjing(CN)
作者
Yang Ling; Liu Kai; Liang Xiaodong; Ma Tao;
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作者单位

School of Information Science and Engineering, Lanzhou university, Lanzhou 730000, China;

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原文格式 PDF
正文语种 eng
中图分类其他计算机;其他计算机;
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An incremental learning algorithm for improved least squares twin support vector machine

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