最小二乘支持向量机的算法研究

Least squares support vector machine algorithm

最小二乘支持向量机(LS-SVM)作为一种新颖的人工智能技术,已越来越广泛地运用于各个学科领域。该文阐述了最小二乘支持向量机的主要思想和基本算法;结合统计学习理论和算例分析了模型参数对模型精度、复杂度和计算量等的影响,为模型参数的确定提供了理论参考;还提出了最小二乘支持向量机的一种改进算法,通过工程实例对比了基于改进算法和原算法的最小二乘支持向量机模型的性能。算例表明该改进算法可以有效地提高模型的整体性能,便于模型在工程上推广使用。

The least squares support vector machine（LS-SVM） has been widely used in various fields for artificial intelligence.This paper describes the basic LS-SVM algorithm.The influence of the model parameters on the model precision,complexity and computing time are analyzed using statistical learning theory and numerical examples.The conclusions give a good guide for determining the model parameters.An improved LS-SVM algorithm is developed with the results showing that the improved algorithm having better overall performance and better efficiency for wider application.