Content-based spam filtering using hybrid generative discriminative learning of both textual and visual features

Author

Amayri, Ola ; Bouguila, Nizar

Author_Institution

Electr. & Comput. Eng. Dept., Concordia Univ., Montreal, QC, Canada

fYear

2012

fDate

20-23 May 2012

Firstpage

862

Lastpage

865

Abstract

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.

Keywords

probability; support vector machines; unsolicited e-mail; Langevin distributions; SVM kernels; content based spam filtering; hybrid generative discriminative framework; hybrid generative discriminative learning; probabilistic Support Vector Machines; spam emails filtering; textual features; visual features; Electronic mail; Feature extraction; Kernel; Probabilistic logic; Support vector machines; Vectors; Visualization; Langevin mixture; SVM; Spam; bag of words; discriminative learning; generative learning; local features; probabilistic kernels;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems (ISCAS), 2012 IEEE International Symposium on

Conference_Location

Seoul

ISSN

0271-4302

Print_ISBN

978-1-4673-0218-0

Type

conf

DOI

10.1109/ISCAS.2012.6272177

Filename

6272177

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3027573