To improve search efficiency and reduce unnecessary traffic in Peer-to-Peer (P2P) networks, this paper proposes a trust-based probabilistic search algorithm, called preferential walk (P-Walk). Every peer ranks its nei...
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ISBN:
(纸本)1595930515
To improve search efficiency and reduce unnecessary traffic in Peer-to-Peer (P2P) networks, this paper proposes a trust-based probabilistic search algorithm, called preferential walk (P-Walk). Every peer ranks its neighbors according to searching experience. The highly ranked neighbors have higher probabilities to be queried. Simulation results show that P-Walk is not only efficient, but also robust against malicious behaviors. Furthermore, we measure peers' rank distribution and draw implications.
In this paper, we investigate one-class and clustering problems by using statistical learning theory. To establish a universal framework, a unsupervised learning problem with predefined threshold η is formally descri...
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The naïve Bayesian classifier (NBC) is a simple yet very efficient classification technique in machine learning. But the unpractical condition independence assumption of NBC greatly degrades its performance. Ther...
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The key issue of Peer Data Management Systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers...
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Analysis of transforming matrices between Bezier basis functions and geometrically continuous basis functions is presented It is shown that G 2 transforming matrix has some relationship with G1 transforming matrix. Ba...
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Emotion study is a multi-disciplinary research subject. In the past three decades, a number of theoretical models of emotion and computer applications have been proposed from different perspectives including psycholog...
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Feature extraction or selection is one of the most importmant steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propos...
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Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas con...
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