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In this paper, in order to properly evaluate the relative importance of priors and observed data in the Bayesian framework, we propose an extended Gaussian mixture model (EGMM) and design the corresponding learning inference algorithms. First, we define the likelihood function of the EGMM and then propose the variational learning algorithm for this EGMM. Moreover, the proposed model and approach are...
In TCM theory, the syndrome is crucial to diagnose diseases and treat patients. In syndrome identification, the relation of symptoms usually correlates with syndrome and represents the pattern of syndrome at symptomatic level. Hence, we learn models for classifying syndromes in depression using 4 different algorithms, which are naive Bayes, Bayes network, SVM and C4.5. From the results of classification,...
E-Learning has been a topic of increasing interest in recent years, effective E-Learning distributed environments should promote high cooperation. This paper proposes a novel E-Learning architecture along with an implementation approach to build efficient E-Learning applications. In this architecture, E-Learning infrastructure uses the Grid technology which chooses the best available resources for...
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