An Improved Algorithm on Least Squares Support Vector Machines

Wang Liejun; Lai Huicheng; Zhang Taiyi

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An Improved Algorithm on Least Squares Support Vector Machines

机译：最小二乘支持向量机的一种改进算法

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The Least Squares Support Vector Machines (LS-SVM) is an improvement on the Support Vector Machines (SVM). Combined the LS-SVM with the Multi-Resolution Analysis (MRA), an improved algorithm-the Multi-resolution Least Squares Support Vector Machines (MLS-SVM) algorithm is proposed in this study. With better approximation ability, the proposed algorithm has the same theoretical framework as the MRA. At a fixed scale the MLS-SVM is a classical LS-SVM. However, the MLS-SVM can gradually approximate the target function at different scales. In experiment, the MLS-SVM is used as nonlinear system`s identification, with better identification accuracy achieved.

机译：最小二乘支持向量机（LS-SVM）是对支持向量机（SVM）的改进。将LS-SVM与多分辨率分析（MRA）相结合，提出了一种改进的算法-多分辨率最小二乘支持向量机（MLS-SVM）算法。该算法具有更好的逼近能力，与MRA具有相同的理论框架。在固定规模上，MLS-SVM是经典的LS-SVM。但是，MLS-SVM可以在不同比例下逐渐逼近目标函数。实验中，将MLS-SVM作为非线性系统的辨识方法，具有较高的辨识精度。

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《Information Technology Journal》 |2008年第2期|共4页
作者
Wang Liejun; Lai Huicheng; Zhang Taiyi;
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中图分类自动化技术、计算机技术;
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An Improved Algorithm on Least Squares Support Vector Machines

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