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Cross-domain learning text classification aims to train an accurate model for a target domain by using labeled text data from a source domain with different but related data distributions. To narrow the data distribution gap between different domains, most of the previous approaches utilize the bag-of-words model to obtain latent features representation of the text. However, this kind of model loses...
With the development of Internet based services, the requirement of keeping keep their vitality and the user viscosity has become an important challenge. Better understanding of users behaviour is an effective way to improve the services lifecycle management. As such analysis of users experience from web log, questionnaire and some other ways have been attached much importance. From previous studies...
Analysis of noisy data gathered from measurement devices is challenging in the power grid. In this study, an effective noisy data regression approach based on general regression neural networks (GRNN) is employed to deal with the problem for remote terminal units (RTU) in power SCADA systems. Experimental results show the proposed model is able to handle noisy data for practical applications, and...
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