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A evolutionary programming is proposed in this paper to automatically design neural networks(NNS) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the task and thereby solve it more efficiently and elegantly. At the same time, different individual NNs are always to find the best collaboration connection during the evolutionary process...
In this work a learning algorithm for visual object tracking is presented. As object representation a fast computable set of Haar-like features is used and a weighted correlation is applied for the matching process. The object tracker utilizes the same set of features that is already calculated for object detection and thus it is possible to reuse features for detection and tracking. The feature's...
In this paper, we proposed a new approach which employed the selfish gene theory to construct virtual population for optimizations. And an incremental learning scheme which based on mutual information entropy was also used to speed up the convergence velocity. Experimental results on several benchmark problems show that, this new approach often performs better than BMDA, COMIT and MIMIC.
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