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In clinical practice, the magnetic resonance imaging (MRI) is a prevalent neuroimaging technique for Alzheimer's disease (AD) diagnosis. As a learning using privileged information (LUPI) algorithm, SVM+ has shown its effectiveness on the classification of brain disorders, with single-modal neuroimaging samples for testing but multimodal neuroimaging samples for training. In this work, we propose to...
Robust scale and rotation estimation is an important and challenging problem in visual object tracking. There have been proposed many sophisticated trackers to track the location of a target accurately, but most of them do not take much attention to the scale and rotation estimation. Inspired by the success of the correlation filters in visual tracking, we proposed a novel scale-and-rotation correlation...
We address the problem of video face retrieval in TV-Series, which searches video clips based on the presence of particular character, given one video clip of his/hers. This is tremendously challenging because on one hand, faces in TV-Series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs highly efficient representation...
Sparse Bayesian learning (SBL) and relevance vector machines(RVM) have received much attention in the machine learning, which as a means of achieving regression. The methodology relies on a parameterized prior that encourages models with few non-zero weights. In this paper, we present a new and efficient algorithm which exploits properties of the marginal likelihood function to enable maximisation...
Core Vector Machine (CVM) is a promising technique for scaling up a binary Support Vector Machine (SVM) to handle large data sets with the utilization of approximate Minimum Enclosing Ball (MEB) algorithm. However, the experimental results in implementation show that there always exists some redundancy in the final core set to determine the final decision function. We propose an approximate MEB algorithm...
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