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

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2 Author(s)
Amayri, O. ; Electr. & Comput. Eng. Dept., Concordia Univ., Montreal, QC, Canada ; Bouguila, N.

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.

Published in:

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

Date of Conference:

20-23 May 2012