Fault Diagnosis Based on MultiKernel Classification and Information Fusion Decision  

作　　者：Mohammad Reza Vazifeh Pan Hao Farzaneh Abbasi 

机构地区：[1]School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

出　　处：《Computer Technology and Application》2013年第8期404-409,共6页计算机技术与应用（英文版）

摘　　要：In machine learning and statistics, classification is the a new observation belongs, on the basis of a training set of data problem of identifying to which of a set of categories （sub-populations） containing observations （or instances） whose category membership is known. SVM （support vector machines） are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes fon^as the output, making it a non-probabilistic binary linear classifier. In pattern recognition problem, the selection of the features used for characterization an object to be classified is importance. Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, impticitly perform a nonlinear mapping 4~ of the input data in Rainto a high-dimensional feature space H. Cover＇s theorem states that if the transformation is nonlinear and the dimensionality of the feature space is high enough, then the input space may be transformed into a new feature space where the patterns are linearly separable with high probability.

关 键 词：Fault diagnosis wavelet-kernel information fusion multi classification. 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TS941[自动化与计算机技术—计算机科学与技术]