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.

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。

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A new bearing fault detection approach based on relevance vector machine (RVM) is presented.

针对轴承故障检测问题，提出一种基于相关向量机(RVM)的故障检测方法。

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In addition, an adaptive kernel relevance vector machine based on PSO is presented to deal with the problem that the regression performance of classical RVM is often influenced by kernel parameters.

此外，针对相关向量机回归计算结果受核参数影响较大的问题，本文还提出一种基于微粒群算法的相关向量机核参数自适应优化方法。

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