支持向量机在分类问题中的应用研究

支持向量机是一种基于统计学习理论的机器学习方法,针对小样本情况表现出了优良的性能,目前被广泛应用于模式识别、函数回归、故障诊断等方面。这里主要研究支持向量机分类问题,着重讨论了以下几个方面的内容。首先介绍了支持向量机分类器算法,并将其应用于数据分类,取得了较高的准确率,所用数据来自于UCI数据集。仿真结果表明该算法具有较快的收敛速度和较高的计算精度。

Support Vector Machines （SVM）is a machine-learning algorithm based on statistical learning theory.Because of the excellent perfor- mance to limited samples,support vector machine is more and more widely used in fields such as pattern recognition,function fitting,fault diagnosis and so on.In this paper,we focused on the SVM classification problems,and such problems are analyzed especially.First,nonlinear classifiers algorithms of support vector machines are discussed and compared.Then they are applied to data classification based on UCI data set.High accuracy is obtained. Finally,The simulation results show that it meets both convergence speed andcalculafion accuracy.