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Spam continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) based techniques have been proposed for spam classification. However, SVM training is a computationally intensive process. This paper presents a parallel SVM algorithm for scalable spam filtering. By distributing, processing and optimizing the subsets of(More)
Latent Semantic Indexing (LSI) has been widely used in information retrieval due to its efficiency in solving the problems of polysemy and synonymy. However, LSI is notably a computationally intensive process because of the comput260 Y. Liu, M. Li, M. Khan, M. Qi ing complexities of singular value decomposition and filtering operations involved in the(More)
P2P networks facilitate people belonging to a community to share resources of interest. However, discovering resources in a large scale P2P network poses a number of challenges. Although Distributed Hash Table (DHT) structured P2P networks have shown enhanced scalability in routing messages, they only support key based exact matches. This paper presents(More)
The explosion of social networking sites has not only changed the way people communicate, but also added a new dimension to the way for searching or investigating people. As users share a wide variety of information on social networking sites, concerns are growing about organisations’ access to personally identifiable data and users are increasingly worried(More)
A combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them, support vector machine (SVM) ensembles with bagging have shown better performance in classification than a single SVM. However, the training process of SVM ensembles is notably computationally intensive, especially when the(More)