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本文通过介绍模式识别中最小二乘支持向量机的基本原理,并应用最小二乘法支持向量机原理包含的三种分类方法对车型进行识别,分析了大量多元分类方法对车型识别的影响,并将实验结果进行对比分析。支持向量机支持向量机(Support Vectormachine,SVM)作为一类新型机器学习方法,由Vapnik等人提出的是,这种方法对小样本、非线性及高维等模式识别问题有更好的解决办法。该方法具有良好的泛化能力,因而在模式识别中得到了广泛应用。
This paper introduces the basic principle of least square support vector machine (SVM) in pattern recognition, and uses the three classification methods contained in Least Squares Support Vector Machine (SVM) principle to identify the models. The influence of a large number of classification methods on vehicle type recognition is analyzed. Experimental results for comparative analysis. As a new type of machine learning method, SVM (Support Vectormachine, SVM) proposed by Vapnik et al. Is that this method has a better solution to the problem of pattern recognition such as small sample size, nonlinearity and high dimension . The method has good generalization ability and has been widely used in pattern recognition.