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In this study, the authors propose a multi-group–multi-class domain adaptation framework to recognise events in consumer videos by leveraging a large number of web videos. The authors’ framework is extended from multi-class support vector machine by adding a novel data-dependent regulariser, which can force the event classifier to become consistent in consumer videos. To obtain web videos, they search...
Recognizing events in consumer videos is becoming increasingly important because of the enormous growth of consumer videos in recent years. Current researches mainly focus on learning from numerous labeled videos, which is time consuming and labor expensive due to labeling the consumer videos. To alleviate the labeling process, we utilize a large number of loosely labeled Web videos (e.g., from YouTube)...
A method based on support vector machine (SVM) is presented to calculate the characteristic impedance of transverse electromagnetic (TEM) horn antennas. The method can be used for computer aided design (CAD) of TEM horn antennas. The results obtained from the SVM model are in very good agreement with the analytical and numerical result.
The online monitoring for NOx emission of coal-fired boilers in power plants is more difficult to achieve. The soft-sensor technology of artificial neural network (ANN) method that was commonly used has not strong generalization ability, but support vector machine modeling-method can solve the problem better. In this paper, a soft-sensor modeling on NOx emission of power station boilers based on least...
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