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This paper proposes an improved SVM method in order to improve the speed of classification when SVM treats with the large training set. Firstly, using RS theory to eliminate redundant information of the large original training data set, secondly, utilizing the idea of probabilities, train an initial classifier with a small training set, and prune the large training set with the initial classifier...
In order to select the most predictive features from network sample data for fault diagnosis, a novel adaptive immune clonal selection algorithm (AICSA) is proposed. By simulating the mechanisms of biological immune system such as immune memory, clone selection and self-adaptation, AICSA achieves the dynamic control of evolution process, which realizes global optimal computing combined with the local...
Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems of pattern recognition. Only a part of samples or support vectors (SVs) plays an important role in the final decision function. But SVs could not be obtained in advance until a quadratic...
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