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Training kernel SVM on large datasets suffers from high computational complexity and requires a large amount of memory. However, a desirable property of SVM is that its decision function is solely determined by the support vectors, a subset of training examples with non-vanishing weights. This motivates a novel efficient algorithm for training kernel SVM via support vector identification. The efficient...
The newly proposed video coding standard, High Efficiency Video Coding (HEVC), has been widely accepted and adopted by industry and academia due to its better coding efficiency compared with H.264/AVC. While HEVC achieves an increase of about 40% in coding efficiency, its computational complexity has been increased significantly. Given this, a high performance AVC to HEVC transcoder is needed urgently...
In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce poor performance measures. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. The proposed method is based on a multilevel framework of the cost-sensitive SVM...
An improved least squares support vector machines (LS-SVM) was proposed to improve the sparse and robust performance of LS-SVM in the small samples prediction. The sparse and robust performance could be improved through adding elements of weighted LS-SVM and robust LS-SVM. We introduced a contrast experiment for ATE parameters prediction control through the three methods of neural network, LS-SVM...
The standard support vector machine (SVM) is celebrated for its theoretically guaranteed generalization performance. However, it lacks sparsity and thus cannot be used for feature selection. Zero norm SVM is ideal in the sense of sparsity while its optimization is prohibitive due to the combinatorial nature of zero norm. In this paper, 1 norm and infinite norm constraints are employed simultaneously...
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