A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in a small set of samples.
提出一种在小样本的情况下，基于多层贝叶斯网络的医学图像语义建模方法。
The results show that it has better performance than the other three classifier on the standard text sample set, and it has some superiority on small set of samples.
结果表明该分类器在标准文本样本集合上的性能好于其他三种分类器，在小样本分类上具有一定优势。
Firstly, sample set is roughly classified using ART to reduce the scale of samples, in training set, and then all small training sets is trained using parallel BP.
首先用ART网络对训练集中的样本进行粗分类，以减小训练集的样本规模，然后用多个BP网络并行地对小训练集进行训练。
There are usually few training samples in the tasks of content-based remote sensing image retrieval, which will lead to over-learning problem while using this small data set for training.
提出一种基于多分类器协同训练的遥感图像检索方法，该方法在不同特征集上分别建立分类器，利用不同分类器的协同性自动标记未知样本，从而有效解决了小样本问题。
There are usually few training samples in the tasks of content-based remote sensing image retrieval, which will lead to over-learning problem while using this small data set for training.
提出一种基于多分类器协同训练的遥感图像检索方法，该方法在不同特征集上分别建立分类器，利用不同分类器的协同性自动标记未知样本，从而有效解决了小样本问题。
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