Content-Based Spam Filtering Using Hybrid Generative Discriminative Learning of Both Textual and Visual Features

被引：0

作者：

Amayri, Ola ^{[1
]}

Bouguila, Nizar ^{[1
]}

机构：

[1] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 2W1, Canada

来源：

2012 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 2012) | 2012年

关键词：

Spam; SVM; Langevin mixture; discriminative learning; generative learning; probabilistic kernels; bag of words; local features;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we propose a hybrid generative discriminative framework for the challenging problem of spam emails filtering using both textual and visual features. Our framework is based on building probabilistic Support Vector Machines (SVMs) kernels from mixture of Langevin distributions. Through empirical experiments, we demonstrate the effectiveness and the merits of the proposed learning framework.

引用

页码：862 / 865

页数：4

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