为此提出了一种微生物发酵的故障诊断新方法，即两个关联向量机分别作为观测器和分类器。

Here a new method for fault diagnosis of microbiological fermentation sensor was provided, i. e. two relevance vector machines were used as observer and classifier respectively.

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将关联向量机应用于高光谱影像分类，实现高维空间中训练样本不足时分类器的精确建模。

The relevance vector machine (RVM) is used to process the hyperspectral image in this paper to estimate the classifiers precisely in the high dimensional space with limited training samples.

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首先采用双支持向量机（SVM）的方法将实时应力信息与机内测试诊断结果相互关联。

A support vector machine (SVM) model is used to correlate the false alarm of BIT to time stress information.

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首先采用双支持向量机（SVM）的方法将实时应力信息与机内测试诊断结果相互关联。

A support vector machine (SVM) model is used to correlate the false alarm of BIT to time stress information.

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