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。
A novel PNN model with training algorithms is proposed for class conditional density estimation.
提出了一种新的类条件密度函数估计的PNN模型及其算法。
Under large samples, it is considerable complex to solve SVM questions by traditional methods. A series of training algorithms are discussed and compared.
在大训练样本情况下，用传统的方法求解SVM问题计算复杂，针对该问题探讨了一系列的SVM训练算法，并对其进行了比较。
The simulations on the main steam-temperature control system of the electric plant boiler show that the controller and its training algorithms are possible and effective.
对电厂锅炉主蒸汽温度控制的仿真结果表明了此控制器及其学习算法的可行性和有效性。
The paper used the Bayes regularization algorithm to train the BP network, the precision and generalization of which are better than the network that uses ordinary training algorithms.
本文采用贝叶斯规则化的训练方法，训练好的BP网络较常用的训练方法具有更好的精度和泛化能力。
There are a few training algorithms for parameter estimation of neural networks, in which Back Propagation(BP)algorithm is the typical algorithm for feed-forward multi-layer neural networks.
神经网络参数估计有许多训练算法，BP算法是前向多层神经网络的典型算法，但BP算法有时会陷入局部最小解。
So far, a major theme in these machine learning articles has been having algorithms generalize from the training data rather than simply memorizing it.
到目前为止，众多有关机器学习的文章中一个重要的主题是利用算法对训练数据进行总结归纳，而不是简单的记忆。
We've already talked a bit about the fact that algorithms may over-fit the training set.
我们已经提到了一点有关算法可能会与训练集过拟合（over-fit）的细节。
Similarly, with machine learning algorithms, a common problem is over-fitting the data and essentially memorizing the training set rather than learning a more general classification technique.
同样，对于机器学习算法，一个通常的问题是过适合(原文为over -fitting，译者注)数据，以及主要记忆训练集，而不是学习过多的一般分类技术。
The existing algorithms of network security situation prediction depended on the initial training data, and the objectivity of predicting results was insufficient.
现有网络安全态势预测算法对初始训练数据依赖性强，预测结果客观性差。
By comparing different neural networks and studding algorithms the following paragraph will discover a training method and neural network with high convergent speed and great accuracy.
本文将通过对比不同的神经网络、不同学习算法找到一种较快收敛速度及较高精度的训练方法和神经网络。
After introducing the structure of the new model, we give the estimation formulas for the parameters of the new model and the algorithms for training and recognition.
本文在给出新模型的框架后，推导了模型参数的估值公式，并给出了模型的训练和识别算法。
Through support vector machine algorithms for gene expression data classification training, SVMs provide a effective way for analysis of gene expression data.
通过支持向量机训练算法对基因表达数据进行分类训练，为分析基因数据提供有效的手段。
Because the error transfer function of rough neural network is not differentiable, genetic algorithms are applied for training the network.
由于粗神经网络的误差传递函数不可微，所以采用遗传算法来训练粗神经网络。
To compare triphone models under different observation densities in Chinese language, three models and their respective algorithms of training and recognizing are constructed.
为了比较汉语三音子模型在不同观测概率情况下的差异，本文构造了三种不同模型，及其训练和识别算法。
In order to getting the effective training data of chemical engineering modeling, two algorithms that fuzzy C-means and fast global fuzzy C-means clustering were used.
分别采用模糊c -均值聚类方法和快速全局C -均值聚类两种算法实现化工建模所需训练数据的有效提取。
The key technologies is proposed, including methods of definition of mining topics, online acquirement of extra large amount of training samples, and algorithms of data mining with high performance.
提出了关键技术，包括：挖掘主题的定义方法、海量训练样本的在线生成和高性能数据挖掘算法。
In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.
当训练样本线性可分时，本文证明前馈神经网络的在线BP算法是有限次收敛的。
The training of the novel model utilizes the maximum likelihood criterion and an effective EM algorithms to adjust model parameters is developed.
新模型的训练采用最大似然准则，并改进了EM算法来调整模型参数。
This paper conveys the application of genetic algorithms (GA) which are used to improve unsupervised training and thereby increase the classification accuracy of remotely sensed data.
本文将遗传算法(GA)应用于非监督训练，提高了遥感数据的分类精度。
In the research of data mining-based intrusion detection, data mining algorithms close rely on high standard training datasets, and this limits the validity and generality of results in this field.
在基于数据挖掘的入侵检测研究中往往紧密地依赖于高标准的训练数据集，这严重制约了这一领域研究成果的有效性和通用性。
The main target of the thesis is: for the shortcoming of the slowly training speed about SVM, we want to find a new SVM accelerated training algorithm based on the existing SVM algorithms.
本文主要的研究工作是：针对支持向量机训练速度慢的问题，在现有支持向量机加速训练算法的基础上，寻找一种新的SVM加速训练算法。
This algorithms has been widely used in machine learning, artificial intelligence, adaptive control, artificial neural network training, Image processing, among other areas.
目前这类算法已被广泛应用于机器学习，人工智能，自适应控制，人工神经网络训练，图像处理等各个方面。
Both of the algorithms based on the context of feature words in sentence of training texts can get a set of feature words that identify the category of a text.
该算法依据训练文本集的特征词句子环境，获取识别文本主题类别的特征词集合。
The authors assume a basic chemistry background and some training in college-level discrete mathematics and algorithms.
那些作者在学院水准离散数学和算法方面假装一个基本的化学背景和一些训练。
Most of manifold alignment algorithms can only give the predictive value of the training set instead of producing a mapping defined everywhere.
大多数流形对齐算法只能给出了训练集上的预测值，而没有给出整个数据空间上的映射关系。
This paper presents approaches to the implementation of virtual training system for fire using some algorithms of real-time rendering, ai and pr, combining with hazards model of fire.
探讨了利用实时三维绘制技术、人工智能和模式识别的相关算法结合火灾的危害模型建立虚拟火灾训练系统的途径。
It introduces the estimation algorithms of single-antenna system, including training sequences, pilots estimation and LS estimation at the beginning.
首先介绍了单天线系统下的信道估计方法，包括基于训练序列，导频的估计算法和LS估计算法。
Through support vector machine algorithms for data classification training, SVMs provide a effective way for analysis of this data.
通过支持向量机训练算法对数据进行分类训练，为分析数据提供有效的手段。
We propose a method, use Unsupervised text Clustering algorithms (UTC) to guid text classification, so as to deal with text classification without training set.
提出了一种用无监督聚类算法指导文本分类的方法，以解决没有训练集的文本分类问题。
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