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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)
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 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)
In the last decades, Gaussian Mixture Models (GMMs) have attracted considerable interest in data mining and pattern recognition. A GMM-based clustering algorithm models a dataset with a mixture of multiple Gaussian components and estimates the model parameters using the Expectation-Maximization (EM) algorithm. Recently, a new Locally Consistent GMM (LCGMM)(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)
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)