Huaxiang Zhang

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Ensembles of classifiers can increase the performance of pattern recognition, and have become a hot research topic. High classification accuracy and diversity of the component classifiers are essential to obtain good generalization capability of an ensemble. We review the methods used to learn diverse classifiers, employ fuzzy clustering with deflection to(More)
How to choose the optimal parameter is crucial for the kernel method, because kernel parameters perform significantly on the kernel method. In this paper, a novel approach is proposed to choose the kernel parameter for the kernel nearest-neighbor classifier (KNN). The values of the kernel parameter are computed through optimizing an object function designed(More)
A new inductive transfer-learning algorithm called NEDRT is presented in this paper in order to improve the classification accuracy of a domain task by using the knowledge learned from labeled data generated from a different domain. NEDRT introduces a novel error function for a constructed neural network by summing a weighted squared difference between the(More)