The speed of the classification is fast without the sample training or pattern matching.
该分类方法避免了样本训练和模板匹配，分类速度快。
This algorithm USES the prediction error threshold to retain the useful information to decrease sample training scale.
该算法利用预测误差阈值进行样本的取舍，在尽量保留有用信息的情况下减小样本训练规模。
Experiments show that this approach performs well in sample training and results in satisfactory verification rate and identification rate.
实验结果表明，文中方法识别具有良好的训练效果，能获得较好的验证率和鉴别率。
The algorithm of face recognition based on kernel principal component analysis(KPCA)can abstract nonlinear features of image and can get better performance under less sample training conditions.
基于核主成分分析（KPCA）的人脸识别算法能够提取非线性图像特征，在小样本训练条件下有较好性能。
In this article, we give a sample outline for an escrima training routine.
在本文中，我们给出一个棍棒术训练日程的示例大纲。
A training sample needs to be pure text, extracted from a sample document of the category in question.
训练样本应该是纯文本，是从涉及的目录的样本文档中提取出来的。
The classification problem is then to find a good predictor for the class y of any sample of the same distribution (not necessarily from the training set) given only an observation [6]:338.
所谓的分类问题就是指对于相同分布的样本x（可以是训练集以外的样本），都能预知其所属的类。
Once completed, ant test takes the sample test documents and tries to classify them using the model that was built during training.
完成后，anttest将尝试使用在训练时建立的模型对示例测试文档进行分类。
To simplify the extraction of document text, the OmniFind/ICM integration can be run in "training mode" (not included in the sample annotator).
为了简化文档文本的提取过程，OmniFind/ICM集成可以运行在 “训练模式” 下（在示例注解器中不包含这个模式）。
Answer: I have received the sample of two software programming training, do not require extensive training, and I am also the programming of particular interest.
回答样本二：我受过系统的软件编程的训练，不需要进行大量的培训，而且我本人也对编程特别感兴趣。
The expense for training material and sample products thereof is the upper limit and Donor has the right to make corresponding adjustment based on the company's operation and management.
其中，产品样品和培训资料的费用部分的金额为上限值，赠予方有权根据企业经营状况进行相应调整。
A scale training algorithm of BP neural network is used, and sample reorganization method is proposed. Its advantage is the fast training speed and good feature extraction ability.
作者使用比例训练的BP算法，提出对训练模式进行样本重组的方法，其特点是训练速度快、特征抽取能力强。
Unfortunately, their small sample size precluded the authors from suggesting the training program had a preventive effect.
不幸的是，他们的小样本阻止作者认为这样的训练计划有预防的作用。
Then simulation results express this method has better detection rate, overall accuracy and false positive rate reduced with less training sample size.
仿真实验结果看出，该方法在训练样本数相对较少的情况下，仍然具有较高的检测率和正确率，同时也具有较低的虚警率。
How to define training sample size and therefore select classifiers is a problem to solve in actual classification considering the cost of acquisition of samples.
考虑到样本获取的代价性，如何根据训练样本的大小来选择有效分类器是实际分类中需要解决的问题。
This paper is mainly concerned with extracting effective features from the recognized or classified signals by selecting wavelet packet basis via given training sample sets.
本文主要研究由给定的训练样本集，如何选择最优小波包基，从被识别或分类的信号中提取具有最大可分性的特征。
Adopted the data processing method of the normalization, choose the training sample of the neural network, the mathematical model of the consumer price index based on BP nerve network predicts set up.
采用归一化数据处理方法，选择神经网络的训练样本，建立基于BP神经网络的居民消费价格指数预测的数学模型。
The fuzzy membership of each sample is defined by affinity among samples, and by the training determine a threshold, noises and outliers are removed, which influence optimal separating hyperplane.
应用基于样本之间的紧密度确定每个样本的模糊隶属度，通过训练确定阀值，去除影响得到最优分类超平面的噪声和野点。
The model was trained with training sample aggregation. The maximum error between the forecasted and real value was 0.97%.
用训练样本集对网络训练后，检验样本的预测结果与实际值最大误差为0.97%。
Training sample set inevitably contains gross error in input signal reconstruction of nonlinear multifunctional sensor.
在非线性多功能传感器的信号重构过程中，训练样本集不可避免地夹杂粗差数据。
In the paper, a new and sufficient method about the selection of the training sample is proposed and also the division of inputting in festivals is operated with a new method by using interpolation.
对于输入模式的划分，在前人的基础上，采用一种新型且有效的方法选取训练样本，对于节假日的负荷，本文对其进行另外的讨论，并提出了一种基于插值的模式选取办法。
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。
We develep a new training method in which a normal orthogonal system is used as sample.
提出了采用标准正交系作为样本的新的训练方法。
The classification accuracy was 89.6% for the training sample and 88.9% for the verifying sample.
训练样本的判别准确性为89.6%，校验样本的判别准确性为88.9%。
When the training sample is very large, RLS algorithm is used to train the networks.
在训练样本很大时，选择利用RLS算法来训练网络。
Results Mix normal distribution model with four normal variables was fitted by the training sample.
结果由训练样本拟合得到四元混合正态模型；
Results Mix normal distribution model with four normal variables was fitted by the training sample.
结果由训练样本拟合得到四元混合正态模型；
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