Title :
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
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;
Conference_Titel :
Circuits and Systems (ISCAS), 2012 IEEE International Symposium on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-0218-0
DOI :
10.1109/ISCAS.2012.6272177
