决策理论是统计模式识别中的一个基本方法。
Bayesian decision theory is a basic method of Statistical Pattern Recognition.
通信信号的分类识别是一种典型的统计模式识别问题。
The classification and identification of communication signal is a typical statistical pattern identification.
统计模式识别，及在面像识别，基因数据分析中的应用。
Statistical pattern recognition, with applications to face recognition, data analysis with microarray.
简述了统计模式识别方法的原理及其在循环冷却水处理中的应用。
The principle of statistical pattern recognition and its application to the treatment of circulating cooling water is introduced in brief.
目前机助分类技术主要着眼于统计模式识别和基于知识的分类决策。
Now the computer_based classification techniques' study has been focusing on the statistical pattern recognition and the knowledge_based classification rule.
本文提出了一个适用于统计模式识别任务的阶层式前馈神经网络模型。
A hierarchical feedforward neural network model particularly suited to practical statistical pattern recognition tasks is proposed in this paper.
人工神经网络技术在统计模式识别领域中的推广能力上成为了一个替代技术。
Artificial neural networks constitute an alternative technique to be used for generalization within the area of statistical pattern recognition.
与统计模式识别模型比较，灰色模式识别具有计算简便、适应范围广的特点。
As far as this research was concerned, the grey relational analysis was superior to the statistical pattern recognition in its simplicity, rapidity and correctness.
根据模仿人类视觉模型，基于文字图像的统计模式识别方法是文字识别取得瞩目进展的基础。
Based on the theory of Visual thinking of human, more excellent progresses for Chinese character recognition have been achieved by the statistical pattern recognition method on the character image.
传统统计模式识别方法进行遥感影像分类时要求数据服从正态分布且难以加入地理辅助数据。
The traditional statistical classifier is suitable in making RS image classification in normal distribution and unsuitable in doing with the data in discrete distribution, such as the geographic data.
本文采用基于统计模式识别的方法，研究了非合作通信与合作通信中通过提取调制信号的星座图特征对调制模式进行识别的问题。
In this research, based on statistical pattern recognition method, we use constellation of modulated signals to classify modulation modes both in non-cooperative and cooperative communications.
已有的调制识别方法，包括基于决策理论的方法和基于统计模式识别的方法，绝大多数都是针对单信号提出的，不能直接用于多信号的识别。
Modulation recognition methods presented for single signal case are not valid for multiple signals' case, including decision theory based and statistic pattern recognition based methods.
此领域与数据挖掘密切相关，并且经常需要使用各种技巧，包括统计学、概率论和模式识别等。
The field is closely related to data mining and often USES techniques from statistics, probability theory, pattern recognition, and a host of other areas.
非线性函数逼近作为统计理论的一个重要分支，在模式识别中有着广泛的应用。
As one of the important branches in statistic theory, the non-linear function has a large application in model-identification.
它涉及到统计，分析和模式识别，行为分析，时间序列分析，预测建模，可视化，因果的研究等等。
It involves statistics, profiling and pattern recognition, behavioral analysis, time series analysis, predictive modeling, visualization, cause-and-effect studies and more.
采用模糊数学中模式识别方法对专题地图统计数据进行分级，并定义了分级精度标准的计算公式。
Statistical data of thematic maps are graded with the method of model distinguishing in mathematics, and a formula for the standard of grading accuracy is given.
支持向量机是统计学习理论的一个重要的学习方法，也是解抉模式识别问题的一个有力的工具。
Support vector machine (SVM) is an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition problems.
该软件集成了模式识别、人工智能、统计学习理论、数据库技术和领域知识等常用的过程工业优化方法。
The software integrated most of the modern optimization methods including database search, pattern recognition, artificial intelligence, statistical learning, and domain knowledge.
支持向量机是统计学习理论的一个重要学习方法，也是解决模式识别问题的一个有力工具。
Support Vector Machine (SVM) is an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition.
数据挖掘技术起源于从统计方法，模式识别，数据库，人工智能，高性能和并行计算和可视化。
Data mining techniques have their origins in methods from statistics, pattern recognition, databases, artificial intelligence, high performance and parallel computing and visualization.
支持向量机是一种基于统计理论的机器学习算法，在解决小样本、非线性及高维模式识别中有独特的优势。
Support vector machine is a kind of machine study algorithm based on statistic theory, it has special advantage in solving small sample, non-linear and high dimension mode recognition.
本文首先对电梯交通模式的类别、特点进行了分析，提出了基于电梯客流统计特性的客流交通模式识别方法。
The thesis analyses the characters of elevator traffic system firstly, further point out the identifying of traffic pattern by the statistics about the elevator traffic.
多变量场异常划分和识别系统，是运用统计分析、模式识别和计算机图形技术研制而成的。
System for sorting and recognition of anomalies in multivariate field was developed using statistical analysis, pattern recognition and computer graph technique.
聚类分析是多元统计分析的方法之一，广泛的应用在模式识别，数据挖掘和决策分析等领域。
Cluster analysis is one of the means of multivate analysis and is widely used in pattern recognition, data mining and decision analysis etc.
数据挖掘是一门交叉性学科，涉及机器学习、模式识别、归纳推理、统计学、数据库、高性能计算等多个领域。
Data mining is an intercrossed subject, involving many fields such as machine learning, model reorganization, induction and deduction, statistics, database and high performance calculation.
提出一种H . 264编码中帧间模式选择的算法，利用帧与帧之间的时间相关性并提出通过模式识别中统计分类的方法对帧间宏块模式进行分类。
In this paper, we have proposed an inter-mode selection algorithm for H. 264, which is based on the time-relativity of the neighboring frames and adopts the idea of statistic classification.
试验表明，该系统可以获得不同缺陷电容器发生局部放电时放电幅值的时间分布谱图，这为直流局部放电的统计分析和模式识别、判断故障类型和介质老化程度打下了良好的基础。
Through the experiments, the magnitude of PD over time in different capacitors were got, which would be used to recognize the PD patterns and to determine fault types and aging degree.
支持向量机(SVM)是基于统计学习理论的一种智能学习方法，可以用来解决样本空间的高度非线性的模式识别等问题。
Support Vector Machine (SVM) is an intellectual learning method based on the statistics theory. The SVM can solve problems of complicated nonlinear pattern recognition of spatial samples.
这种方法基于统计分析的原理，并将模式识别加入其中，形成一套独特的自学习方法，提高了泄漏检测和定位的精确度，并有效降低了误报警率。
Because of a particular character of self-learned, this new method increases the precision of detecting and locating, and reduces the incidence of false alarms.
这种方法基于统计分析的原理，并将模式识别加入其中，形成一套独特的自学习方法，提高了泄漏检测和定位的精确度，并有效降低了误报警率。
Because of a particular character of self-learned, this new method increases the precision of detecting and locating, and reduces the incidence of false alarms.
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