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We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images
to keeping the original idea in TWSVM, still the edges of our method lie in considerably less computing time with respect to TWSVM, which is comparable to that of GEPSVM. Experiments tried out on standard datasets disclose the effectiveness of our method. Keywords: TWSVM; dual QPPs; approximate.
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