例如，某些基本的神经网络，它们的感知器只倾向于学习线形函数(通过划一条线可以把函数输入解析到分类系统中)。
For instance, a certain kind of basic neural network, the perceptron, is biased to learning only linear functions (functions with inputs that can be separated into classifications by drawing a line).
Bayesian网络或神经网络等技术使用表达能力非常强的模型，力求生成无偏向的分类器来“描述”文档集。
Techniques such as Bayesian networks or neural networks use highly expressive models, which try to produce a non-biased classifier in order to "describe" a corpus of documents.
采用基于灵敏度分析的BP神经网络模型作为基分类器，进一步剔除冗余基因。
BP neural network based on sensitivity analysis is used as base classifier to learn the subsets and redundant genes are further removed.
用BP人工神经网络分类器进行识别，结果表明矩特征的识别率较高，说明该方法具有良好的应用效果。
The recognition with BP artificial neural net grader shows that the recognition rate of moment features is rather high, this indicates that this method has better applicable effect.
还利用三种飞机缩比模型的暗室测量数据，研究了时延神经网络分类器中时延单元数目对分类精度的影响以及分类器的分类性能。
The effect of time delay unit number on classification precision and the performance of TDNN classifier using three typical aircraft dark room data measured with scale model were studied.
盒维数的简单统计结果可以作为PQ神经网络分类器的输入特征量。
After simple statistics, the dimension can act as the input vector of ANN for PQ classification.
BP算法具有智能性和自学习性的特点，因此，本文提出采用BP神经网络来构造邮件分类识别器。
BP algorithm has aptitude and auto-learning characters, so my paper choose BP neural net algorithm to set up mail classification and recognition model.
本文以统计理论为基础，主要讨论在计算机上用软件模拟实现的神经网络分类器。
Based on the theory of statistics, this dissertation investigates neural network classifiers realized with software simulation in the computer.
特征量被预处理后，输入到集成bP神经网络分类器中分类。
Finally, the features are preprocessed, then classified by integrating BP neural networks.
本篇论文提出一个类神经网路分类器来学习多类的分离模糊资讯。
This paper presents a multiclass neural network classifier to learn disjunctive fuzzy information in the feature space.
本文对系统中的车牌定位和字符分割、特征提取、BP神经网络分类器等模块进行了较详细的研究。
In this paper, the system of license plate location and character segmentation, feature extraction, BP neural network classifier etc modules have had a more detailed research.
提出了一种粗糙模糊神经网络分类器的模型。
A model of rough fuzzy neural network classifiers is proposed.
研究了一种用模糊集表示火箭发动机故障模式的神经网络分类器。
A neural network classifier that utilizes fuzzy sets as failure classes of a liquid propellant rocket engine is studied.
采用BP神经网络实现分类器。
人工神经网络与气体传感器相结合，用于识别、分类、诊断和预测，将进一步提高气体检测系统的智能水平。
Artificial neural network together with gas sensors, which is applied to identification, classification, diagnosis and prediction, will further improve the intelligence of gas detection system.
针对一类基于模糊感知器的神经模糊分类器，分析了隶属函数限制条件对分类结果的影响。
For a neuro_fuzzy classifier based on the fuzzy perceptron, this paper analyses how membership function constraints affect the classification result.
实验结果表明：算法优化后的神经网络分类器不但学习速度快，还能保证分类精度。
Experiment shows neural network classifier that is optimized by algorithm could not only have fast learning speed but also ensure accuracy of classification.
同时，对缺陷的自动分类方法、神经网络分类器和用于对带钢质量进行自动分级的专家系统做了简要介绍。
At the same time, the automatic classification method to the defects, the nerve net classified units, and the experts system to classifying the surface quality were reviewed.
使用该B-P神经网络作为汉字的分类器，可以大大提高车牌汉字的识别率。
These features are used to train a B-P neural network, it is a classifier and can improve greatly the recognition rate of Chinese characters.
此外，提出用带输出误差死区的混合BP算法训练神经元分类器，提高了网络学习训练速度和分类准确性。
Further, a hybrid BP algorithm with dead interval of error is derived for training the neural classifier in order to increase training speed and classification accuracy.
利用BP神经网络分类器及选择的特征值对缺陷进行了模式分类。
Pattern classification of flaw is carried out with BP neural network and the feature selected.
提出了一种在线进行调制识别的系统模型，并给出一种基于神经网络的快速收敛分类器算法。
This paper mainly proposes an algorithm that the speedy constringency classifier of neural networks and proposes a system model of online modulation recognition.
经实验证明，基于改进后的RBF网络具有更少的隐含神经元，但仍然保持了基于RBF网络分类器的准确率。
Checked by the experiments, the improved RBF network has less hidden neural units than before, at the same time keep the accurate of RBF based classifier.
为了验证特征的有效性，使用最近邻及概率神经网络分类器进行了目标识别，得到满意的识别率。
In order to validate character validity, use NearestNeighbor (NN) and probabilistic neural network (PNN) classification identify target, gain content identification probability.
同时本文将机器学习和相关反馈结合起来用于图像检索，在实验中使用了K -NN、BP神经网络和支持向量机分类器。
At the same time, we used relevance feedback and machine learning used in image retrieval. K-NN, BP neural network and support vector machine classifiers were used in experiments.
同时本文将机器学习和相关反馈结合起来用于图像检索，在实验中使用了K -NN、BP神经网络和支持向量机分类器。
At the same time, we used relevance feedback and machine learning used in image retrieval. K-NN, BP neural network and support vector machine classifiers were used in experiments.
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