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Keywords: Self-training Semi-supervised classification Semi-supervised clustering Fuzzy c-means Support vector machine a b s t r a c t 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(More)
Feature extraction is a vital part in EEG classification. Among the various feature extraction methods, entropy reflects the complexity of the signal. Different entropies reflect the characteristics of the signal from different views. In this paper, we propose a feature extraction method using the fusion of different entropies. The fusion can be a more(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)
Face recognition has attracted considerable concerns in recent years. In practical applications, there are generally a small amount of labeled face images and a lot of unlabeled ones can be available. In this paper, we introduce a semi-supervised face recognition method where semi-supervised LDA (SDA) and Affinity Propagation (AP) are integrated into(More)