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The advantages of Principal Component Analysis (PCA) in dimension reduction are obvious, while the classifying methods are various. PCA, applied to Bayesian classification with the minimium risk, is adopted to realize classifying and testing the learning samples in this paper, eventually accurately realizing the classifier training towards the new input sample and then detecting the testing effect.

The Design of Bearing Fault Diagnosis Classifier Based on the Bayesian Classification