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Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing(More)
According to the current situation of the credit risk assessment in commercial banks, a hybrid intelligent system is applied to the study of credit risk assessment in commercial banks, combining rough set approach and support vector machine (SVM). The information table can be reduced, which showed that the number of evaluation criteria such as financial(More)
Sparse unmixing is based on the assumption that each mixed pixel in the hyperspectral image can be expressed in the form of linear combination of a number of pure spectral signatures that are known in advance. Despite the success of sparse unmixing based on the L<inf>0</inf> or L<inf>1</inf> regularizer, the limitation of these approach focuses on analyzing(More)
Reinforcement learning (RL) [1] differs from traditional supervised machine learning in the sense that it not only considers short-term consequences of actions/decisions, but also long-term outcomes. Because of recent advances in deep learning, model-free deep reinforcement learning (DRL) has proven successful in various applications, as with the success of(More)
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