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The kernel function plays an important role in the classification of support vector machines (SVM). In order to solve the problem that a single SVM kernel function can not achieve optimal learning ability and generalization ability in recognition classification at the same time, here we present a new combined kernel function by analyzing and comparing the characteristics of various kernel functions...
Many articles research network scale-free nature from the angle of the topology, this research from the angle of the dynamic equation of the scale-free of network. So far network operation, there is no exact definition, in this article we simply introduce several kinds of network operation. A class of network models, called the compound models, are constructed for understanding and comparing network...
Based on previous work on regional temporal mammogram registration, this study investigates the combination of image features measured from single regions (single features) and image features measured from the matched regions of temporal mammograms (temporal features) for the classification of malignant masses. Three SVM kernels, the multilayer perceptron kernel, the polynomial kernel, and the gaussian...
In this paper, we construct a unified framework for dimensionality reduction, for simplicity we call it essential kernel principal component analysis (EKPCA). Some of well-known dimensionality reduction methods, such as kernel principal component analysis, locally linear embedding, Laplacian eigenmaps, Isomaps, diffusion maps are subject to this framework.
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