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The Cloud data services, specifically, key/value stores and NoSQL database that require a large number of index lookups that fetch small amount of data. Random I/O becomes the critical performance factor. However, compared with sequential read, the efficiency of random read is very low. Our experiment will explain this. File I/O operation is closely associated with the implementation of I/O mechanism...
Space-variant motion deblurring is a key component in image processing system. Previous deblurring methods often do not hold in practice due to the space-variant motion blur in the imaging process. In this paper, we presented a modified Richardson-Lucy method for removing space-variant motion blur from a single image, which is also effective for typical space-invariant motion deblurring. Comprehensive...
Object category recognition is a challenging task due to the low level and non-discrimination in visual representation. Most previous methods concentrate to find better high level visual features. Recently, optimally integrating various features to solve the problem attracted more interests. In this paper, we provide a novel method for object category recognition by improving the popular bag-of-words...
Group Lasso is an efficient regularized least-square regression algorithm, and is now being used as a computationally feasible method to select grouped variables. In this paper, we address the issue of estimation consistency of the group Lasso with special diagonal matrix. We derive sufficient condition for the consistency of group Lasso under practical assumptions, such as model misspecification...
Group Lasso is a recently proposed regression method that can be used to select group variables. When studying the consistency condition of the regularization path of group Lasso, we assume that the groupings of the univariate variables are known and fixed, that is, the group structure is given. In this paper, we address the issue of the influence of group structure on the group Lasso consistency...
SVM has the convenient superiority in the classification. But the insufficiency is required the classed samples ahead of time. This affects its widespread application. The paper tries to take full of DEA methodpsilas objective advantages and SVM modelpsilas machine learning advantages. Using DEA method classes the samples data, so as to the training samples have strong reliability. Then, multi-layer...
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