The neural networks structure design, learning samples and training algorithms are expounded.
阐明了神经网络状态选择器的结构设计、样本选取及训练方法。
Secondly, to extract learning samples from the MADM problem, an approach to estimate the utility functions for attributes is presented.
其次，提出了基于属性效用函数估计的学习样本构造方法，从决策问题本身抽取学习样本。
The affection of learning samples and network parameters on prediction accuracy was discussed, the best network parameters were selected.
讨论了模型的学习样本、网络参数对预测精度的影响，选出最佳网络参数配置。
A prastical neural network of BP model is acquired after trained with a learning samples set, which consists of materials selection knowledge.
利用训练样本使一个BP神经网络学习选择材料的知识，利用测试样本验证此网络的能力。
To overcome the shortage of historical data, the increment of learning samples are got by clustering analysis the time series data from Ticket sale record.
为了克服历史数据不足的问题，设计了通过时间序列聚类分析进行学习样本集的积累的方法。
In the condition of selecting the learning samples properly, the artificial neural network has the obvious advantage in the inverse designing the electronic lens.
可以看到在较好的选取学习样本情况下，神经网络技术在电子透镜的逆设计中有着明显的优越性。
Experiments demonstrated that this approach has good detection ability performance and needs less learning samples, which makes it suitable for many types of defect and textured material.
实验结果表明，该方法检测效果好，且要求学习样本少，适用于不同缺陷类型和各种检测问题。
The SVM method is based on seeking on the Structural Risk Minimization by few learning samples supporting, and it has important feature such as good generalization and classification performance, etc.
支持向量机方法基于小学习样本条件下，通过寻求结构风险最小，以期获得良好的分类效果和泛化能力。
The two samples included with this article provide a great starting point to learning more about XDIME forms.
本文所附带的两个示例为更多地了解XDIME表单提供了一个良好的起点。
Samples of active learning techniques employed by the course faculty are also included.
主动学习技巧之范例亦由系上课程教授提供。
Samples of using concordancing in vocabulary learning and teaching are proposed in the last section of this part.
最后作者提出在词汇学习和教学中使用语料索引的范例。
The equipment research cost estimation model is constructed by learning from the typical samples.
在此基础上通过对典型样本的学习，建立装备研制费用预测模型。
The support vector machine is a learning algorithm, which has a good classification ability for limited training samples.
支撑矢量机是一种能在训练样本数很少的情况下达到很好分类推广能力的学习算法。
With limited samples, SVM has stronger ability of generalization in comparison with existing machine learning algorithm.
与现有的机器学习算法相比，在样本有限的情况下，支撑矢量机具有更强的分类推广能力。
In unsupervised learning, only learning to network with some samples, rather than provide an ideal output.
在无监督学习中，只向网络提供一些学习样本，而不提供理想的输出。
Support Vector Machines(SVM) are developed from the theory of limited samples Statistical Learning Theory (SLT) by Vapnik et al. , which are originally designed for binary classification.
支持向量机（SVM）是建立在统计学习理论基础上的一种小样本机器学习方法，用于解决二分类问题。
SVM solves practical problems such as small samples, nonlinearity, local minima, which exist in most of learning methods, and has a bright future.
支持向量机方法较好地解决了许多学习方法面临的小样本、非线性和局部极小点等问题，具有很好的应用前景。
Finally, taking data from CAE as samples; the BP neural network of warping-shrinkage prediction model is established by designing the network structure and selection of learning algorithm.
最后以数值仿真得到的数据为样本数据，通过设计网络结构和选用学习算法，建立并得到基于BP人工神经网络的翘曲——收缩预测模型。
The method can transfer the learning problem into a second planning to acquire the optimal solution according to the principle of structure risk minimum under limited samples situation.
该算法能针对在样本有限的情况下，采用结构风险最小化准则，把学习问题转化为一个二次规划问题来获得最优解。
So a novel promising machine learning technique specifically developed for analyzing little amount of samples, SVM (Support Vector Machine), will be more suitable in practical industrial application.
因此在实际的工程应用中，支持向量机（SVM）作为一种新型的小样本建模分析工具是更适合的。
ICF can classify unknown samples as the traditional classifier. It also has some functions such as multi-experts decision, pre-classifying and learning.
智能分类器不但可以对未知样本进行分类识别，还具有多专家决策、预分类、学习等功能。
So, the semi-supervised learning method by learning a small number of labeling samples and a large number of samples to establish classifier came into being.
如此，通过对少量已标记样本和大量未标记的样本进行学习从而建立分类器的半监督学习方法应运而生。
As one algorithm of the machine learning based on the statistical learning theory, Support Vector machine (SVM) is specifically to the small samples learning case.
支持向量机是一种基于统计学习理论的机器学习算法，能够较好地解决小样本的学习问题。
The algorithm selects training samples by local sample density, to reduce the training samples and thus to improve the speed of learning.
该算法根据样本的局部密度选择训练样本，减少参加训练的样本数量，提高学习速度。
The third part: the preparation of their use of the questionnaire survey conducted on the samples and samples were related to memory, attention and learning emotional experiment.
第三部分：采用自己编制的调查问卷对样本进行调查，并对样本进行了有关记忆力、注意力及学习情绪的实验。
The learning of Backpropagation Neural Network (BPNN) aimed at lowering the classification error, usually assuming that all the samples had equal price when misclassifications were made.
传统的反向传播神经网络(BPNN)学习以分类错误最小为目标，通常假定在分类错误时所有样本的代价完全相同。
This was further demonstrated with the success of their computer learning models in being able to identify each participant based solely on their samples.
这进一步证实了他们仅凭参与者样本就能识别出每个参与者的计算机学习模型是成功的。
This was further demonstrated with the success of their computer learning models in being able to identify each participant based solely on their samples.
这进一步证实了他们仅凭参与者样本就能识别出每个参与者的计算机学习模型是成功的。
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