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According to the characters of SVM, an improved binary-tree SVM is proposed for multi-class problems. Furthermore aiming at the initial problems about the choice of kernel function and parameters for SVM, an ensemble method is presented to establish ensemble SVM. Here the improved SVM is used as weak learning machine. The new ensemble SVM can improve the performance of single binary-tree SVM. At the...
A novel spam filtering algorithm based on 2v-SVM is proposed on the basis of researching of existing spam filtering algorithms in the paper. 2v-SVM can address the difficulties that arise when the class frequencies in training data do not accurately reflect the true prior probabilities of the classes, which is more superiority than standard SVM. Experiment results show that this method can effectively...
This paper provides a novel multi-class classification algorithm, which combines adaptive resonance theory with support vector machine principle. It improves the one-against-one classification of support vector machine. The algorithm adopts adaptive resonance theory network to fuse the classifiers' results and does not adopt voting principle. When the outputs of classifiers approach zero and the algorithm...
In order to solving fault diagnosis of analog circuit with tolerances, noise, circuit nonlinearities and small sample sets, a novel multi-class classification algorithm which combined binary tree SVMs multi-classification based on self-organizing map nerve network (SOMNN) clustering roughly was proposed. The robustness characteristic of SOMNN based on the separability between pattern classes and support...
In order to extract the significant edges of the medical image without the interference of the internal noise and the external noise, a new medical image edge extraction algorithm is proposed. We use the information measure to remove the internal noise. According to the characteristics of wavelet transform and our requirements, we design a new quadratic B spline wavelet to discriminate the external...
Aiming at to the characteristics of analog circuits with tolerances, noise and poor controllability and testability of the internal nodes, a novel method of fault diagnosis based on the multi-frequency feather extraction technique and decision directed acyclic graph support vector machines (DDAGSVMs) multi-class classification was proposed. Generally non-linear support vector machines were applied...
For the complexity and confusion in fault diagnosis of blast furnace, a new artificial intelligent algorithm is presented to solve this problem. The support vector machines (SVMs) are one type of large margin classifier based on statistical methods. With the property of dealing with high dimension data, studying small quality of samples and training large data sets, it is feasible to use it to make...
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