基于高斯核支持向量机的图像垃圾文本特征检测

Prashant Kumar, M. Biswas
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引用次数: 12

摘要

随着互联网的发展和电子邮件在我们日常生活中的重要性日益增加,垃圾邮件已经成为一种普遍现象,造成严重的威胁,因为它会产生不受欢迎的电子邮件。图片垃圾邮件是一种电子邮件垃圾邮件,其中文本信息嵌入在图像中,呈现为图片。本文提出了一种基于高斯核的支持向量机分类器来检测垃圾邮件。在我们的实验中,我们将公开可用的数据集与SVM和基于高斯核的分类器一起使用,表明我们的方法在测量F-measure,召回率,准确性和精密度方面比考虑的分类器具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SVM with Gaussian kernel-based image spam detection on textual features
With the growth of the internet and the increasing importance of emails in our daily lives, spams have become a common phenomenon posing serious threats, as it gives rise to undesired emails. Image spam is a type of email spam in which the textual message is embedded within an image presenting it as a picture. This paper proposes a Support Vector Machine (SVM) with Gaussian kernel based classifier for detection of spam. In our experiment, we have used publicly available datasets with SVM with Gaussian kernel based classifier showing that our approach gives good performance over considered classifiers for measurement of F-measure, recall, accuracy and precision.
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