Shiliang Sun

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A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed(More)
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent target functions and thus has the merit of accelerating function evaluations when predicting the output of a new example. This framework makes use of Fenchel-Legendre conjugates to(More)
PURPOSE To assess the effect of preoperative embolization on blood loss during surgical repair of bone metastases from renal cell carcinoma and provide long-term follow-up. PATIENTS AND METHODS Sixteen patients with bone metastases underwent preoperative embolization. Polyvinyl alcohol (PVA) particles were used for 13 patients (three with additional(More)
A visual attention based approach is proposed to extract texts from complicated background in camera-based images. First, it applies the simplified visual attention model to highlight the region of interest (ROI) in an input image and to yield a map, named the VA map, consisting of the ROIs. Second, an edge map of image containing the edge information of(More)
An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies the same number of nearest neighbors for each test example. It is known that the value of k has crucial influence on the performance of the kNN algorithm, and our improved(More)
An algorithm of additive audio watermarking based on SNR to determine a scaling parameter /spl alpha/ is proposed. The visually recognizable binary watermark image could be embedded in the original signal wavelet domain in the algorithm. The intensity of embedded watermarks on the different audio segments can be modified by adaptively adjusting the scaling(More)
Support vector machines (SVMs) are theoretically well-justified machine learning techniques, which have also been successfully applied to many real-world domains. The use of optimization methodologies plays a central role in finding solutions of SVMs. This paper reviews representative and state-of-the-art techniques for optimizing the training of SVMs,(More)
PURPOSE To evaluate the safety and assess the role of endovascular therapy in a variety of conditions related to celiac and mesenteric vascular occlusive disease. Patients and methods Our retrospective study population included 25 consecutive patients (mean age, 66 years), in whom 28 procedures were performed on 26 stenosed or occluded mesenteric vessels(More)
One challenge in the current research of brain–computer interfaces (BCIs) is how to classify time-varying electroencephalographic (EEG) signals as accurately as possible. In this paper, we address this problem from the aspect of updating feature extractors and propose an adaptive feature extractor, namely adaptive common spatial patterns (ACSP). Through the(More)
This paper proposes a subject transfer framework for EEG classification. It aims to improve the classification performance when the training set of the target subject (namely user) is small owing to the need to reduce the calibration session. Our framework pursues improvement not only at the feature extraction stage, but also at the classification stage. At(More)