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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...
In this paper, we propose an approach for multiple target localization in wireless sensor network by employing the compressive sensing (CS) theory, which provides a novel framework for recovering the signal with far fewer sampling values than traditional methods, under the assumption that the signal is sparse. The sparsity in our localization approach is reflected by the location of targets, which...
The availability of high-resolution (HR) remote sensing multispectral imagery brings opportunities and challenges for land cover classification. The methodology of multiscale segmentation is wildly accepted for feature extraction and classification in HR image. However, the relationship among chosen scale parameters, selected features, and classification accuracy is less considered. A classification...
In the conventional regularized learning, training time increases as the training set expands. Recent work on L2 linear SVM challenges this common sense by proposing the inverse time dependency on the training set size. In this paper, we first put forward a Primal Gradient Solver (PGS) to effectively solve the convex regularized learning problem. This solver is based on the stochastic gradient descent...
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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