提出了一种大规模数据集的训练样本选择方法。
A new method is proposed for sample selection in large data set.
本文主要从训练样本选择和预测算法两个方面进行了研究。
The paper studied Ultra-short term the load forecasting from two aspects:data preprocessing and forecast method.
以入侵检测系统中的分类器设计为例，研究分类器训练样本选择问题。
Taking the example of designing classifier in intrusion detection system, the selection of training samples for classifier is studied.
同时，在模糊C-均值聚类基础上选择训练样本比起直接基于真实地物图选择，减少了主观因素对训练样本选择的影响，因此取得了更高的分类精度。
Selecting train sample on the basis of fuzzy C-mean clustering decreased subjective factor affecting selecting train sample, so higher classification accuracy can be achieved.
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。
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.
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。
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.
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。
In order to improve the accuracy of model prediction, the training samples should be representatively prepared.
在模糊c -均值聚类的基础上选择训练样本，可以提高训练样本的准确度，满足了训练样本所需的单一性原则。
Selecting train sample on the basis of fuzzy C-mean clustering can improve accuracy of train sample, singleness of train samples can be satisfied.
其中一组通过选择适当窗函数的窗宽以及增加训练样本数量，预测精度可达到86.7%，另一组为72%。
The 86.7% prediction accuracy can be achieved by selecting appropriate window width of the window function and increasing the samples of the training set for one group, and 72% for the other.
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 paper is mainly concerned with extracting effective features from the recognized or classified signals by selecting wavelet packet basis via given training sample sets.
在训练样本很大时，选择利用RLS算法来训练网络。
When the training sample is very large, RLS algorithm is used to train the networks.
最后对训练样本数据个数、神经元个数的选择进行了探讨和经验总结。
Finally, discuss and research how to select the number of the neural units and the training data.
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。
A prastical neural network of BP model is acquired after trained with a learning samples set, which consists of materials selection knowledge.
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。
In order to improve the accuracy of model prediction, the training sample...
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 algorithm selects training samples by local sample density, to reduce the training samples and thus to improve the speed of learning.
当然读者也可以自行用训练样本训练网络，不过要特别注意训练样本的选择，否则可能造成识别率很低。
Of course, the reader can also be its own training network with training samples, but pay special attention to the training samples, otherwise it may result in the recognition rate is very low.
当然读者也可以自行用训练样本训练网络，不过要特别注意训练样本的选择，否则可能造成识别率很低。
Of course, the reader can also be its own training network with training samples, but pay special attention to the training samples, otherwise it may result in the recognition rate is very low.
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