支持向量机的实现(包括线性核函数、高斯核函数等),以及基于SVM的垃圾邮件分类实现过程(MATLAB)

上传者: amyniez | 上传时间: 2022-07-06 21:05:47 | 文件大小: 923KB | 文件类型: ZIP
支持向量机(support vector machines,SVM)是一种二分类模型,它将实例的特征向量映射为空间中的一些点,SVM 的目的就是想要画出一条线,以 “最好地” 区分这两类点,以至如果以后有了新的点,这条线也能做出很好的分类。SVM 适合中小型数据样本、非线性、高维的分类问题。 SVM 最早是由 Vladimir N. Vapnik 和 Alexey Ya. Chervonenkis 在1963年提出,目前的版本(soft margin)是由 Corinna Cortes 和 Vapnik 在1993年提出,并在1995年发表。深度学习(2012)出现之前,SVM 被认为机器学习中近十几年来最成功,表现最好的算法。

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