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A model based on support vector machine (SVM) active learning and Karhunen-Loeve(K-L)feature extracting is proposed for oil-immersed transformer fault diagnosis, and a SVM active learning algorithm with the Euclidian distance based on Mercer function is introduced to select the training sample data. The K-L transform is used to extract the characteristics of the sample data set, and the sample data...
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