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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper examines the problem of approximating Kernel functions by random features. The main result is that using an L1 regularisation one can use only O(1/\epsilon) random features that to obtain an \epsilon accurate approximation to kernel functions. The paper develops Sparse random features algorithm which is analogous to functional gradient descent in boosting. The algorithm require O(1/\epsilon) random features which compares extremely favourably with the state of the art which requires O1/\epsilon^2) features.