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When dealing with large data sets, the traditional support vector machine (SVM) needs long training time which is aroused by the complexity of computation for kernel function. Moreover, if there are noises in a given training set, the classification accuracy rate of the traditional SVM is usually low. To overcome the shortcomings above, the algorithm of SVM based on half-suppressed fuzzy c-means clustering...
In order to recognize shaft orbits exactly and quickly, a novel recognition method based on Walsh spectrum and support vector machine is proposed in this paper. Firstly, distance vector between the point on the shaft orbit and its center is calculated and obtained. Secondly, the distance vector is transformed by Walsh orthogonal matrix, and obtained Walsh spectrum has property of invariance to rotation,...
This paper proposes that support vectors include redundant information after analyzing Kernel's geometrical structure and researching data dependant method for improving support vector machine (SVM). Redundant information confuses the law of a learning problem. Data dependant method on improving SVM is based on Riemannian geometry theory and could exclude redundant information. Reasoning and experiments...
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