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。
A new regression algorithm of an adaptive reduced relevance vector machine is proposed to estimate the illumination chromaticity of an image for the purpose of color constancy.
摘要提出了一种新的自适应约简相关向量机回归算法来估计图像的光照色度以达到色彩一致性目的。
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。
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。
Relevance feedback algorithm based on support vector machine and rough set for image retrieval is approached.
研究基于支持向量机和粗糙集的相关反馈图像检索算法。
At the same time, we used relevance feedback and machine learning used in image retrieval. K-NN, BP neural network and support vector machine classifiers were used in experiments.
同时本文将机器学习和相关反馈结合起来用于图像检索，在实验中使用了K -NN、BP神经网络和支持向量机分类器。
At the same time, we used relevance feedback and machine learning used in image retrieval. K-NN, BP neural network and support vector machine classifiers were used in experiments.
同时本文将机器学习和相关反馈结合起来用于图像检索，在实验中使用了K -NN、BP神经网络和支持向量机分类器。
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