In order to improve the accuracy of model prediction, the training sample...
为了提高模型的预测精度，在训练样本的选择上还应具有一定的代表性。
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。
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%。
The validity of structural damage detection using neural network strongly depends on the training sample.
用神经网络进行结构损伤检测、分析的有效性在很大程度上取决于训练样本的好坏。
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%。
In the classification experiment, we find that the number of the support vector is far less than the number of the training sample number.
在分类实验中，我们发现支持向量的数量远远小于样本数，这为我们解决大规模数据问题提供了方法。
The training sample sets and test sample sets for the ANN model were prepared from the real production of the continuous casting production.
采用生产现场数据制做了预测模型的训练样本集和测试样本集。
In this model, a logistic regression function is induced from multiple decision trees, which are built based on different training sample sets respectively.
该模型通过对使用多个样本集分别训练出的多棵决策树预测函数进行逻辑回归来得到最终的预测函数。
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。
This algorithm accumulates distribution knowledge of the training sample while the incremental training is proceeded, and thus makes it possible to discard samples optimally.
该算法通过在增量学习中逐步积累样本的空间分布知识，使得对样本进行有选择地遗忘成为可能。
Support vector machine (SVM) is an effective method for resolving regression problem, however, traditional SVM is very sensitive to noises and outliers in the training sample.
支持向量机(SVM)是解决回归问题的一种有效的方法，但传统的支持向量机对样本中的噪声和孤立点非常敏感。
Neural network need not establish accurate mathematics model, it sums up the relation implicit in the systematic input output through studying input output training sample data.
神经网络不需要建立精确的数学模型，只是通过学习输入输出训练样本数据，就可归纳出隐含在系统输入输出中的关系；
Meanwhile, the length of time on training sample was determined as the recent complete economic cycle when using artificial neural network for railway freight volume forecasting.
并提出在利用人工神经网络进行铁路货运量预测时，训练样本的时间长度为最近的一个完整经济周期。
The experiments on the ORL face database show that the recognition rate of the proposed method is high when pose, illumination condition, face expression and training sample number change.
在OR L人脸库的测试结果表明，在姿态、光照、表情、训练样本数目变化的情况下，该算法都具有较好的识别率。
It gives each training sample a fuzzy membership property, and embodies the different contribution of training samples for classification result and emphasizes the importance of edge samples.
在训练样本中增加模糊隶属度属性，从而体现训练样本对分类的不同贡献，突出边缘样本的作用。
The results of simulation experiments show effective reduction for large-scale training sample set and improvement of operation efficiency of this algorithm, guaranteeing the classification precision.
仿真实验表明，该算法能在保证不降低分类精度的前提下，对较大规模的样本进行有效的缩减，提高运算效率。
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。
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。
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