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Background and objectives: Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the...
Grammar teaching and learning have always been important and difficult parts in L2 Chinese. This paper demonstrates a method for automatically extracting and recommending Grammar Points to L2 Chinese teachers and learners. First, a L2 Chinese grammar syllabus is reconstructed based on a corpus of international Chinese teaching materials. Second, a regular expression-based learning algorithm is explored...
Social annotations provide additional document description contributed by online users and they have been explored for improving search performance. However, most existing methods need offline analysis of the whole tagged corpus, which is computationally expensive and cannot fit specific queries well. In this paper, we propose to use tags for document re-ranking. Specifically, we first estimate document...
In order to get better semantic annotation performance, block-global features are extracted as low-level visual features for image semantic annotation. Specifically, wellknown global feature extraction method, namely two-dimensional principal component analysis (2DPCA) is applied to extract the image block-global features. Unlike typical image annotation methods which use local features or global...
In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods...
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