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Semi-supervised classification has become an active topic recently and a number of algorithms, such as Self-training, have been proposed to improve the performance of supervised classification using unlabeled data. In this paper, we propose a semi-supervised learning framework which combines clustering and classification. Our motivation is that clustering(More)
Face recognition is one of the most important applications of machine learning and computer vision. The traditional supervised learning methods require a large amount of labeled face images to achieve good performance. In practice, however, labeled images are usually scarce while unlabeled ones may be abundant. In this paper, we introduce a semi-supervised(More)
BACKGROUND Action observation (AO) has the potential to improve motor imagery (MI) practice in stroke patients. However, currently only a few results are available on how to use AO effectively. OBJECTIVE The aim of this study is to investigate whether MI practice can be improved more effectively by synchronous AO than by asynchronous AO. METHODS Ten(More)
Recently, semi-supervised learning has received much attention in data mining and machine learning, and a number of algorithms are proposed to discuss how to make good use of the unlabeled data. Some algorithms deal with the unlabeled data in an exact way, in which each unlabeled sample is assigned to one single class and then treated as a labeled sample.(More)
Recent methods based on midlevel visual concepts have shown promising capabilities in the human action recognition field. Automatically discovering semantic entities such as action parts remains challenging. In this paper, we present a method of automatically discovering distinctive midlevel action parts from video for recognition of human actions. We(More)