Chord: A scalable peer-to-peer lookup service for internet applications
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Towards a Fully Distributed P2P Web Search Engine
FTDCS '04 Proceedings of the 10th IEEE International Workshop on Future Trends of Distributed Computing Systems
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A Peer-to-Peer (P2P) based decentralized personalized information access system called PeerBridge for edge nodes of the Internet network is proposed to provide user-centered, content-sensitive, and high quality information search and discovery service from Web and P2P network timely. The general system architecture, user modeling and content filtering mechanism of PeerBridge are discussed in detail. Moreover in order to only find information which users are interested in, a new heterogeneous neural network ensemble (HNNE) classifier is presented for filtering irrelevant information, which combines several component neural networks to accomplish the same filtering task, and improves the generalization performance of a classification system. Performance evaluation in the experiments showed that PeerBridge is effective to search relevant information for individual users, and the filtering effect of the HNNE classifier is better than that of support vector machine, Naïve Bayes, and individual neural network.