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Regular harmony search algorithm has defects such as prematureness and convergence stagnation when treating complicated optimization problems, which influence on optimizing the performance of BP neural network. For function optimization problems, we analyze two key parameters of HS algorithm: harmony fine adjustment probability and the harmony adjustment range, which affect the performance in searching...
Conventional multiple-instance learning (MIL) algorithms for image annotation usually neglect concept dependence (i.e., the relationship between positive and negative concepts) and feature selection (i.e., which feature modality is suitable for a specific concept) problems, which have significant influence on the annotation performance. In this paper, we propose a novel concept-dependent algorithm...
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