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The χ 2 kernel based support vector machines (SVMs) have achieved impressive performances in many image and text classification tasks. As a nonlinear kernel method, however, it does not scale well to large scale data, because the computation of the χ 2 kernel matrix is intractable. To address this challenge, we propose a sparse random projection method to linearly approximate the χ...
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