Learning with local and global consistency(LLGC) algorithm can effectively label a data,but it is helpless for...
Ming Li,Xiaoli Zhang,Xuesong Wang School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116 China
Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.
GAO Yang, WANG Xuesong, CHENG Yuhu, PAN Jie School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China
＜正＞In order to improve the generalization performance of support vector machine（SVM）,a kind of ensemble SVM us...
Ruhai Lei,Xiaoxiao Kong,Xuesong Wang School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116 China
