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Forecasting road traffic conditions requires an accurate knowledge of the spatio-temporal dependencies of traffic flow in transportation networks. In this article, a Bayesian network framework is introduced to model the correlation structure of highway networks in the context of traffic forecast. We formulate the dependency learning problem as an optimization problem and propose an efficient algorithm...
In this paper, we propose a Bayesian nonparametric approach for modeling and selection based on the mixture of Dirichlet processes with Dirichlet distributions, which can also be considered as an infinite Dirichlet mixture model. The proposed model adopts a stick-breaking representation of the Dirichlet process and is learned through a variational inference method. In our approach, the determination...
Dataset used in financial distress prediction is unbalanced. The traditional machine learning method such as neural network and support vector machine is premise with the hypothesis that the class distribution is basically balanced. The classification of unbalanced dataset inclines to the relative majority samples results in the lower identification of the minority while the conventional down-sampling...
In this paper,the concept of intellectual information circle is presented,because of the poor accuracy rate about searching informarion in this circle,an approach of classification about information based on bayes algorithm is discussed. The result of the experiment shows that comparing with the old system, the apply of bayes algorithm for the intellectual information circle improves the accuracy...
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