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Outlier detection, as a type of one-class classification problem, is one of important research topics in data mining and machine learning. Its task is to identify sample points markedly deviating from the normal data. A reliable outlier detector needs to build a model which encloses the normal data tightly. In this paper, an improved one-class SVM (OC-SVM) classifier is proposed for outlier detection problems. We name this method OC-SVM with minimum within-class scatter (OC-WCSSVM), which exploits the inner-class structure of the training set via minimizing the within-class scatter of the training data. This can construct a more accurate hyperplane for outlier detection, such that the margin between the training data and the origin in a higher dimensional space is as large as possible, while at the same time the decision boundary around the normal data is as tight as possible. Experimental results on a synthetic dataset and 10 real-world datasets demonstrate that our proposed OC-WCSSVM algorithm is effective and superior to the compared algorithms.

We consider the problem of a base station multicasting a scalable video source to multiple clients, with the clients requiring different video reconstruction qualities. The base station first transmits the video source packets using hierarchical network coding (HNC). Due to the lossy channels, some clients may not receive sufficient packets to reconstruct the video at its desired quality. These clients will request the base station to retransmit. The goal of the proposed retransmission method is to satisfy all clients' requests with the least number of retransmissions. It further offers a "soft" video reconstruction in that the quality of the reconstructed video keeps improving as the clients receive more packets.