早期的观察结果具有一定的代表性，但样本空间较小。
Early observations were assumed to be representative, but were based on small samples.
试验，结果，样本空间，事件，机率，机率公理。
Experiments, Outcomes, Sample Spaces, Events, Probability, Axioms of Probability.
在相对合理的样本空间中采用偏最小二乘回归建立模型。
Then it builds a model by partial least squares regression on the obtained sample space.
同时由于减小了可用于对比的信息样本空间，也增加了解密的难度。
It reduces the contrastive encrypted information swatch space which can be used, but it increases the difficulty to unlock.
根据样本空间的内积特性，提出一种无需迭代学习的自适应变结构神经网络。
A new adaptive variable neural network is developed according to the inner product feature of sample space. This method does not need to iterate learning.
利用样本空间排序法给出了参数优良的置信下限与计算置信下限的递推公式。
The method of constructing the lower confidence limit is based on sample space ordering.
文章介绍了在小样本空间中，采用动态调整样本空间数据的手段来提高预测精度；
The method of dynamic adjusting data to improve the forecast precision in the small sample field is introduced.
由于样本空间比较小不具备统计学意义，但是目前它识别的准确率已经达到了80%~90%。
The sample size was too small to be statistically significant, but it was accurate about 80 to 90 percent of the time.
基于正交和随机方法安排了有限元仿真试验，获得了用于非线性电磁建模的样本空间。
To obtain sample space used to establish the nonlinear regression model, the FEM simulation tests were arranged with the methods of orthogonal and random experimental design.
这两种算法均利用邻居节点间的通讯约束关系构建样本空间，从而提高算法的定位精度。
Both algorithms take advantage of the communication relationship with the neighbor node, build the sample space, to improve the positioning accuracy.
使用样本空间排序法给出了指数分布情形下不相同定时截尾试验平均寿命置信下限的估计方法。
Sample space order method is used to give the lower confident limits of mean life under unequal time truncations for exponent distribution.
前向网络利用反向传播算法训练多层网络，使训练后的网络较好地拟合样本空间中各点的函数值。
Feedforward networks use back propagation algorithm to train a multi-layer network. After training, the multi-layer network can fit the function in the data space very well.
基于K型区间删失数据，利用样本空间排序法给出参数优良的置信下限和计算置信下限的递推公式。
In the case of K type interval censored data, the lower confidence limit of parameter is studied based on the order relation established in the sample space.
对于线性不可分的样本空间，需要寻找核函数，将线性不可分的样本集映射到另一个高维线性空间。
As for the undivided linear sample space, the kernel function is needed to map onto another high dimension linear space.
采取运行机理建模和神经网络建模相结合的方式，把输入样本空间进行划分，实现基于混合专家网络的建模。
In the new method, input samples are divided into sub-space and hybrid expert neural networks model is realized.
支持向量机(SVM)是基于统计学习理论的一种智能学习方法，可以用来解决样本空间的高度非线性的模式识别等问题。
Support Vector Machine (SVM) is an intellectual learning method based on the statistics theory. The SVM can solve problems of complicated nonlinear pattern recognition of spatial samples.
试验表明，只要样本空间映射合理，这种自组织无监督的神经网络具有很好的自学习功能，在工程中具有广泛的应用前景。
The test proves that if prototype space mapping is reasonable, then this unsupervised learning ANN has good self - learning function. It has good application in engineering.
基于图的学习是近几年来半监督学习中一个相当活跃的方向，它用图来描述样本空间，利用近邻点的位置来控制标记信息的传播。
Graph-based learning is a very active direction of semi-supervised learning in recent years. It describes the sample space by graph, and USES neighbors to spread label information in point cloud.
将三维可视化技术用于激光共聚焦显微镜层切图像，可以帮助生物研究者更清楚地观察样本空间形态或样本内部物质的空间分布情况。
With the aid of 3d visualization on LSCM images, the researchers can observer the surface structures in the specimens or the internal structures of the specimens more clearly.
对于在特征空间中寻找特征模式，一般是通过假设分布函数一次性对样本空间进行分离的方法去试图获得特征空间的样本总体分布规律。
In order to find out the feature patterns from multi-dimension space, the conventional approach is to separate feature space by assuming the distributed functions of all features in one time.
基于这一问题本文通过构造统计量对所给的样本点进行选择，剔除对模型的构造有很大影响力的样本，从而获得一个相对合理的样本空间。
Based on this problem, this article selects the sample points by constructing statistics. First, it removes the outliers to have a relatively reasonable sample space.
基于这一问题本文通过构造统计量对所给的样本点进行选择，剔除对模型的构造有很大影响力的样本，从而获得一个相对合理的样本空间。
Based on this problem, this article selects the sample points by constructing statistics. First, it removes the outliers to have a relatively reasonable sample space.
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