首页|期刊导航|东华大学学报（英文版）|Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis
东华大学学报（英文版）2006，Vol.23Issue(1)：12-17,6.
Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis
Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis
摘要
Abstract
To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.关键词
support vector machines/data fusion/Bayesian theory/fault diagnosisKey words
support vector machines/data fusion/Bayesian theory/fault diagnosis分类
化学化工引用本文复制引用
..Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis[J].东华大学学报（英文版）,2006,23(1):12-17,6.基金项目
This work was supported by the National Key Fundamental Research Project of China (2002cb312200), the National High Technology Research and Development Program of China (2002AA412010), and in part supported by the Natural Science Foundation of China (60575036). （2002cb312200）
