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支持向量机的分类——基于乳腺组织电阻抗特性的乳腺癌诊断

于 2022-02-01 发布 文件大小:8.37 kB
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支持向量机(Support Vector Machine,SVM)是一种新的机器学习方法,其基础是Vapnik创建的统计学习理论(Statiscal Learning Theory,STL)。统计学习理论采用结构风险最小化(Structural Risk Minimization,SRM)准则,在最小化样本点误差的同时,最小化结构风险,提高了模型的泛化能力,且没有数据维数的限制。在进行线性分类时,将分类面取在离两类样本距离较大的地方;进行非线性分类时通过高维空间变换,将非线性分类变成高维空间的线性分类问题。本章将详细介绍支持向量机的分类原理,并将其应用于基于乳腺组织电阻抗频谱特性的乳腺癌诊断。

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