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Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or to demote some target products. In recent years, fake review detection has attracted(More)
It is well-known that many online reviews are not written by genuine users of products, but by spammers who write <i>fake reviews</i> to promote or demote some target products. Although some existing works have been done to detect fake reviews and individual spammers, to our knowledge, no work has been done on detecting spammer groups. This paper focuses on(More)
In classification, semi-supervised learning occurs when a large amount of unlabeled data is available with only a small number of labeled data. In such a situation, how to enhance predictability of classification through unlabeled data is the focus. In this article, we introduce a novel large margin semi-supervised learning methodology, using grouping(More)
In classification, semisupervised learning involves a large amount of unla-beled data with only a small number of labeled data. This imposes great challenge in that the class probability given input can not be well estimated through labeled data alone. To enhance predictability of classification, this article introduces a large margin semisuper-vised(More)
In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great challenge in that it is difficult to achieve good classification performance through labeled data alone. To leverage unlabeled data for enhancing classification, this article introduces a large margin(More)
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature , their performances largely depend on the tuning parameters that balance the trade-off between model fitting and model sparsity. Existing tuning criteria(More)