该类学习机也是在少训练样本集上构造的。
The reduced training set is used to form the learning machines.
BP网络训练样本由有限元模型模态分析所得。
BP network train sample mode analyse the income by finite element model.
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.
同时将实验结果分为两组：训练样本和测试样本。
Meanwhile we divided the results into two parts: training samples and testing samples.
它较好地匹配了树的复杂性和训练样本量及错分率界。
It well matches the tree complexity to the training data and the misclassification rate bound.
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.
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。
A training sample needs to be pure text, extracted from a sample document of the category in question.
只要训练样本可靠，采用该方法建模可以达到比较高的精度要求。
Modeling with this method can achieve high precision if the training samples are reliable.
用周期函数，有限项傅立叶级数，作为激励函数来获取训练样本。
A periodic function, finite Fourier series, is used to activate the actuator for obtaining training samples.
利用支撑矢量机具有更好的推广能力，可以使用较少的训练样本。
Because of the better generalization performance of SVM, less training samples are needed.
训练样本与测试样本分别朝融合特征空间投影，从而得到识别特征。
After training samples and test samples are respectively projected towards the fusion feature space, recognition features are accordingly gained.
该方法能大大减少训练样本，同时保证故障覆盖率，有一定创新性。
The method can reduce stylebook , ensure the fault rate and it is innovative.
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.
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.
提出一种从训练样本提取基于超盒表示的模糊规则的方法，用于模式分类。
In this paper, we discuss a new method for rule extraction based on hyper-box representation.
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 classification accuracy was 89.6% for the training sample and 88.9% for the verifying sample.
神经网络的训练采用一阶梯度优化算法，利用点堆中子动力学模型产生训练样本。
The first order gradient optimization algorithm is employed to train the network. The training samples stem from the neutron kinetics of the point-reactor.
在进行训练时，将训练样本导入Workbench中，并确保与样本相关联的目录是正确。
For the training itself, import the training samples into Workbench and make sure that the categories associated with the samples are correct.
基于多层核主成分提取估计器需要将调制信号的训练样本根据各自的频率进行分层。
The estimator based on kernel principal component extraction requires to stratify the training samples of interested signals with respect to their respective frequencies.
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%。
The model was trained with training sample aggregation. The maximum error between the forecasted and real value was 0.97%.
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。
The support vector machine is a learning algorithm, which has a good classification ability for limited training samples.
采用灰色理论中的等维新息思想构建训练样本，建立了等维新息神经网络预测模型。
A new neural network model is established based on the concept of equal dimension and new information in grey theory.
二种方法对训练样本的分类正确率达100%。据此模型预报了若干个矿点为锡矿区。
The correct classification rate is 100% for training samples by the two methods, and some targets are predicted as potential Sn ore-field.
在神经网络负荷预测实际应用中，突出的问题是训练样本大、训练时间长、收敛速度慢。
In application of neural networks based short-term load forecasting model, the main problems are over many training samples, thus resulting long training time and slow convergence speed.
对如何从一般正交三轴转台速率实验获取训练样本及对网络的学习训练给予了详细的介绍。
The approach to obtain swatch from general orthogonal three axis-rate-input experiments is analyzed in detail and the nets training is also provided.
对大规模训练样本的支持向量机训练问题进行探索，提出了一种基于正交表的并行学习算法。
Explores the training problems of support vector machine with large training pattern set, and a new parallel algorithm based on orthogonal array is presented.
本文采用的方法在解决大规模训练问题（如11000个训练样本）时表现出的性能令人满意。
When used to solve the convex quadratic programming problems with super large scale of training samples(11000 training samples), the algorithm designed in this paper works better.
此外，很容易把新的训练样本添加到以前训练好的分类器中，便于提高故障诊断结果的准确性。
In addition, newly trained patterns can easily be supplemented to the already trained classifier, thus facilitating the improvement of the accuracy of diagnosis results.
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。
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.
本文对径向基函数网络提出了一种新的学习算法，利用最小均熵差准则对训练样本进行模式聚类。
This paper presents a new leaning method for radial basis function network, minimum mean entropy difference criterion algorithm is used to get pattern cluster of training sets.
应用推荐