Q-MKL: Matrix-induced Regularization in Multi-modality Learning for Neuroimaging of Alzheimer’s Disease
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چکیده
Notice that in general, while the number of kernels may be large, it is unlikely that the size of Q (quadratic in the number of kernels, not examples), will dominate the combined size of all of the kernels. If so (as in a majority of computer vision problems), it may be advantageous to employ second-order methods to solve (1) for β in terms of w. Perhaps the best known of these second order methods is Newton’s method [1] with the following update: β ← (β −H−1g) where the Hessian H and gradient g are
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تاریخ انتشار 2012