分析了每种模型的性质和适用范围，设计了针对不同模型的模式分类器。
The property and applicable range of each model is analyzed, and the Pattern classifier is also designed with respect to each model.
结合组合式电脑茶叶拣梗机的模式分类问题，讨论了模式分类器智能化及实现策略。
The intelligent pattern classifier and its realization techniques are discussed according to the classing process in a computer control combined tea-stalk reject system.
本文将二者结合起来，用小波变换抽取特征、用自适应共振art网络作模式分类器来识别手写数字。
This paper combines the two aspects to recognize handwritten digits by using wavelet transform to extract feature and Adaptive Resonance Theory (ART) Neural Networks for Classification.
提出了一种基于最小分类错误(MCE)训练的采用多层感知器(MLP)结构的模式分类器设计方法。
An improved design method on pattern classifier based on multi-layer perceptrons (MLP) by means of minimum classification error (MCE) training was proposed.
采用B - P型反向传播神经网络构成的智能化模式分类器，对此五类声发射信号进行识别，获得了满意的效果。
An intelligent pattern classifier with B-P neural network is used in recognition of those five kinds of AE signals successfully.
本文提出了一种基于模式类特征空间统计分布的模糊隶属度函数模型，可有效地反映模式在特征空间中的真实分布，用于模式分类器输入特征的模糊化可获取更好的识别性能。
In this paper a model of discrete fuzzy membership function based on statistical distribution of features of pattern is presented. It is used for the fuzziness of input features of classifier.
因此，要运行Mahout的分类器，您首先需要训练模式，然后再使用该模式对新内容进行分类。
Thus, to run Mahout's classifier, you need to first train the model and then use that model to classify new content.
模式识别问题不仅与表示方法有关，也跟用于在分类器设计各个阶段进行训练和测试的用例集有关。
A pattern recognition problem is not only specified by a representation, but also by the set of examples given for training and evaluating a classifier in various stages.
把某一图像的某种特征进行度量并交给分类器，是模式识别的重要环节。
Measure some feature of one image and take it to classifier, this is an important step of pattern recognition.
分类器设计是模式识别系统中的关键步骤之一。
The classifier design is a key step for pattern recognition systems.
目标识别技术属于模式识别理论的研究范围，其关键在于特征提取和分类器的设计。
The target identification technology belongs to the research scope of the pattern recognition, its key lies in the characteristic withdraw and the sorter design.
该文介绍了BP网络的学习过程以及从模式识别角度应用BP神经网络作为分类器进行机械故障诊断。
The paper introduces the studying process of the BP network and USES the BP network for the mechanical failure diagnoses as assorted organ in the mode identification.
在实际应用中，一般的近邻分类器由于模式处理量过大，且难以在线和快速获得最佳近邻数等原因，而受到了限制。
In practice applications, generic near neighbor classifier was limited for the amount of pattern processing was very large and was difficult to get the best data quickly on line.
提出了一种基于核的多类别模式识别算法(简称核子空间法，KSPM)，依据此算法建立了多故障分类器。
A novel multi-class classifier with kernels, namely kernel Subspace Methods (KSPM), was presented, and a multi-fault classifier based on the algorithm was constructed.
结果表明，以CP神经网络构筑的故障模式识别器有很强的非线性映射能力，可对机械设备故障模式进行正确分类。
The result indicates that based on CP neural network, the fault pattern recognition system has strong nonlinear mapping ability, therefore it can be used to correctly classify the mechanical faults.
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.
多分类器系统能够在一定程度上弥补单个分类器的缺陷，因此它在模式识别中得到了广泛的应用。
Since multiple classifier systems can to some extent improve the performance of classification, the technique has been widely used in various fields of pattern recognition.
本文对多分类器融合模式识别的设计方法进行了研究。
This paper studies the design of pattern recognition system based multiple classifiers combination.
多分类器组合方法可以在一定程度上弥补单个分类器的不足，提高分类性能，因此，它在模式识别领域得到广泛的应用。
Since multiple classifier systems(MCS) can improve the performance of classification, the technique has been widely used in various fields of pattern recognition.
感知器是一种有用的神经网络模型，可以对线性可分的模式进行正确分类。
Perceptron is a kind of useful neural network model and can classify the classification of the detachable linearity correctly.
多分类器联合是解决复杂模式识别问题的有效办法。
A combination of multiple classifiers is a powerful solution to the difficult pattern recognition problem.
多分类器组合是解决复杂模式识别问题的有效办法。
Multiple classifiers ensemble is an effective method to solve complex classification problems in pattern recognition field.
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.
支持向量机分类器克服了当前常用的模式识别方法的缺点，有效提高了识别率。
Support vector machine classifier overcome the shortcoming of the present and commonly used pattern-recognition methods, and has improved the recognition rate effectively.
介绍了一种基于模糊模式识别以及向量空间模型提取特征向量的中文文本分类器的设计与实现。
This paper introduces the design and implementation of the Chinese text categorizer based on the fuzzy recognition and the extraction of the characteristic vector with the vector space model.
介绍了一种基于模糊模式识别以及向量空间模型提取特征向量的中文文本分类器的设计与实现。
This paper introduces the design and implementation of the Chinese text categorizer based on the fuzzy recognition and the extraction of the characteristic vector with the vector space model.
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