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.
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。
A mix model with the subspace classifier and BP neural network classifier was realized, which is used in handwritten English letter and number recognition.
最后，用子空间分类器和BP神经网络分类器构造了一个混联模型，用于手写英文字母和数字的识别。
This paper presents a multiclass neural network classifier to learn disjunctive fuzzy information in the feature space.
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 mainly proposes an algorithm that the speedy constringency classifier of neural networks and proposes a system model of online modulation recognition.
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。
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.
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。
In this paper, considering the features of remote sensing images, we proposed a remote sensing image classifier using radial basis function neural network.
针对遥感图象分类的特点，提出了一种径向基函数神经网络的遥感图象分类器。
A combined classifier is designed. There are two recognition ways used in the classifier. They are the least distance pattern recognition method and the BP neural network pattern recognition method.
设计了一个二级组合分类器，该分类器综合使用了最小距离和BP神经网络两种模式识别方法。
Here, the HMM is employed to produce a best speech state sequence which is warped to a fixed dimension vector and the RBF neural network is used as classifier.
该方法首先利用HMM生成最佳语音状态序列，然后用函数逼近技术产生对最佳状态序列进行时间规正，最后通过RBF神经网络进行分类识别。
A model of rough fuzzy neural network classifier was presented by combining rough set and fuzzy neural network.
结合粗糙集和模糊神经网络提出了一种粗糙模糊神经网络识别器的模型。
The forth-order and sixth-order cumulants of received signal are adopted for features extraction while RBF neural networks with a new designed training cost function being used for classifier.
采用信号四阶和六阶统计量提取信号特征，使用新设计的误差函数训练RBF神经网络，使得识别的效率和正确度得到了明显的改善。
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.
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。
The manual neural network has become more and more important as a classifier. Learning from the environment adaptively and generalizing were the most advantages of neural network.
人工神经网络日渐成为一种重要的分类工具，其最大益处就在于它善于对环境的自适应学习，并且具有并行处理泛化能力。
A neural network classifier that utilizes fuzzy sets as failure classes of a liquid propellant rocket engine is studied.
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。
Then, combining IMD-Isomap and generalized regression neural network, which has a good ability for approximation, a classifier is proposed.
然后，结合泛化回归神经网络，设计出一种分类器。
A new method of designing the classifier for intrusion detection is proposed based on neural networks, which is the alternative covering algorithm of multi-layer neural networks.
文章提出了一种应用人工神经网络进行入侵检测分类器设计的新方法，即采用多层前向网络的交叉覆盖算法进行入侵检测分类器的设计。
First selects texture features based on the gray level co-occurrence Matrix and then EBP-OP neural network is used as a classifier. The experimental results show that this method is very effective.
首先运用灰度共生矩阵提取图像的纹理特征，然后用EBP - OP算法对提取的纹理特征进行分类，并在此基础上实现一组纹理图像的检索，实验证明这种方法是有效的。
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.
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。
A hybrid classifier is formed by combining the traditional classification method with an artificial neural algorithm to significantly improve the rafe of recognition.
鉴于人脸识别问题的特殊性，将传统分类方法与人工神经网络方法结合起来，构造了一个混合分类器，从而极大地提高了识别率。
SFAM is an incremental neural network classifier. It is a simple and fast version of Fuzzy ARTMAP (FAM). Both FAM and SFAM produce the same output given the same input.
SFAM是一个改进版神经网络分离器，是模糊ARTMAP的简化和快速版本。对于相同的输入FAM和SFAM具有相同的输出。
Because neural networks have a very strong ability in pattern recognition, an NN classifier is used in the multi-parameter recognition of fault signals.
充分利用了神经网络极强的模式分类能力，用神经网分类器对故障信号进行多参量识别。
Because neural networks have a very strong ability in pattern recognition, an NN classifier is used in the multi-parameter recognition of fault signals.
充分利用了神经网络极强的模式分类能力，用神经网分类器对故障信号进行多参量识别。
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