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Instance-Based Spam Filtering Using SVM Nearest Neighbor Classifier

机译:基于实例的垃圾邮件过滤使用SVM最近邻分类

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In this paper we evaluate an instance-based spam filter based on the SVM nearest neighbor (SVM-NN) classifier, which combines the ideas of SVM and k-nearest neighbor. To label a message the classifier first finds k nearest labeled messages, and then an SVM model is trained on these k samples and used to label the unknown sample. Here we present preliminary results of the comparison of SVM-NN with SVM and k-NN.
机译:在本文中,我们基于SVM最近邻(SVM-NN)分类器来评估基于实例的垃圾邮件滤波器,其结合了SVM和K最近邻居的思想。要标记消息,分类器首先查找最近标记的消息,然后在这些k个样本上培训SVM模型,并用于标记未知样本。在这里,我们呈现SVM-NN与SVM和K-NN比较的初步结果。

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