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Kernel learning is an important learning framework in machine learning, whose main idea is a mapping from input space to feature space induced by kernel function which yields a linear separation problem in the feature space. However, the generalization ability of kernel learning, which may lead to over-fitting of training data, has not been formally taken into consideration in previous literatures...
In ensemble learning, several base learners are combined together in some way to get a stronger learner. Good ensembles are often much more accurate than individual learners that make them up. Ensemble pruning searches for a good subset of ensemble members that performs as well as, or better than the original ensemble. We analyze accuracy, diversity and generalization ability of base learners for...
The transient performance of Electronic Current Transformer (ECT) has a strong impact on the transient protection applications and fault transient recording in smart grid. This paper investigates in detail the transient performance of TECT-10 by theoretical modeling, simulation and experiment. Study shows that the maximum lower cut-off frequency of TECT-10 is a critical factor affecting its transient...
In this paper, we address the computational complexity issue in Sparse Representation based Classification (SRC). In SRC, it is time consuming to find a global sparse representation. To remedy this deficiency, we propose a Local Sparse Representation based Classification (LSRC) scheme, which performs sparse decomposition in local neighborhood. In LSRC, instead of solving the l1-norm constrained least...
Current approaches for generating wrappers for web page extraction suffer from the requirement of huge amount of labeled training pages to obtain satisfying results. On the other hand, the quality of data extracted by fully automatic methods is not reliable. In this paper, we propose a novel method to facilitate wrapper generation by combining wrapper induction and page analysis approaches. In addition...
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