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Internet security is seriously threatened by spam spreading, and content-based spam filtering has become one of effective spam-filtering methods. Aiming at the practical problems, we propose an active learning based method which takes naive Bayesian means as basic classifiers. This method randomly initialize a small training set to generate basic classifiers, and then use them to classify mails, which...
Conditional Random Fields (CRFs) are widely used in machine learning and natural language processing fields. A number of methods have been developed for CRF training. However, even with state-of-the-art algorithms, the training of CRF is still very time and space consuming. This make it infeasible to use CRFs in large-scale data analysis tasks. This paper proposes an efficient algorithm, HOCT, for...
One of the main difficulties in machine learning is how to solve large-scale problems effectively, and the labeled data are limited and fairly expensive to obtain. In this paper a new semi-supervised SVM algorithm is proposed. It applies tri-training to improve SVM. The semi-supervised SVM makes use of the large number of unlabeled data to modify the classifiers iteratively. Although tri-training...
In this paper, we propose a novel learning-based face hallucination framework built in DCT domain, which can recover the high-resolution face image from a single low-resolution one. Unlike most previous learning-based work, our approach addresses the face hallucination problem from a different angle. In details, the problem is formulated as inferring DCT coefficients in frequency domain instead of...
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