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This letter presents a new iterative algorithm for efficient 3-D Laguerre-based finite-difference time-domain (FDTD) method. A new perturbation term and the Gauss–Seidel method are introduced in the algorithm. The theoretical analysis in the frequency domain shows that the splitting error introduced by the new perturbation term grows slower than that of the original one at the high frequency range...
It is an extreme challenge to produce a nonlinear SVM classifier on very large scale data. In this paper we describe a novel P-packSVM algorithm that can solve the support vector machine (SVM) optimization problem with an arbitrary kernel. This algorithm embraces the best known stochastic gradient descent method to optimize the primal objective, and has 1/?? dependency in complexity to obtain a solution...
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