At present some commonly used fault diagnosis methods are based on the large samples data, however we employ the small samples data in the real project, therefore its application has been limited.
目前一些常用的故障诊断方法都是以大样本数据为基础的，但通常在实际工程中得到的故障一般都是小样本数据，使其应用受到了一定的限制。
In the situation of small samples, it belongs to systems reliability integration by math that we estimate the flight reliability of missile using systems and subsystems testing data.
在小子样情况下，利用系统和分系统试验数据对导弹的飞行可靠性指标进行评定，从数学上说是系统可靠性综合问题。
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。
On the basis of test data, this paper gives the probability distributions of the limit tensile strain of large full graded samples and small wet screened ones.
根据实测资料，给出了全级配混凝土大试件和与其对应湿筛后混凝土小试件拉伸极限应变的概型分布；
WENONAH HAUTER: "Why are they using three studies with very small samples that the data is not available to the public, and one of the studies is nineteen years old?"
豪特：“为什么他们采用了三项标本非常少的研究结果，且研究数据不对公众公开？同时其中一项研究结果已19年之久？”
Oriented to the left vs. right hand motor imagery based BCI, research was carried out for the small number of samples from the public data sets of the 2nd International BCI Algorithm Contests.
该部分，使用第二届国际BCI竞赛的相关公开数据，针对想象左右手运动脑电信号采用离散小波变换方法进行了特征提取；
Training the multi-fault classifier only needs a small quantity of fault data samples in time domain, and does not need signal preprocessing for extracting signal features.
应用结果表明，不必进行信号预处理以提取特征量，只需要用少量的时域故障数据样本建立故障分类器。
Our method gains better estimation of data distributions and mitigates the unrepresentative problem of small-size training samples.
我们的方法得到了数据分布的较好估计，能够减轻小样本的无代表性问题。
This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples.
该方法利用大量的未标识数据进行有效聚类，并将聚类结果用于小样本情形下的支持向量机分类。
This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples.
该方法利用大量的未标识数据进行有效聚类，并将聚类结果用于小样本情形下的支持向量机分类。
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