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A framework for a new type of estimation of distribution algorithms (EDAs) is developed. It is similar to the Bayesian optimization algorithm (BOA) except that it replaces Bayesian network model with estimation of schema distribution based on maximum entropy. As structure learning of Bayesian network is not needed, it reduces the computational cost. The experimental results show that the new algorithms...
The Internet enables learners to be brought together where they can cooperate in learning in groups without space and time limitations. Interaction is a critical success factor that affects group learning. In this study, we propose a useful grouping method to help teachers improve group-learning in e-learning by first establishing effective groups based on the naive Bayes method. Personal characteristics...
Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missing parts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances...
This paper proposes a new iterative approach for digital image matting. It combines pre-segmentation and matting into an unified approach and extracts good matte iteratively within a well-defined Bayesian framework based on a few user strokes on foreground and background regions. This method does not need a well specified trimap, which refers to a pre-segmented image with definitely foreground, definitely...
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