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.
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 main idea of which consists of three parts: the discriminating feature analysis of the images, the statistical modeling of face and non-face classes, and the Bayes classifier for face detection.
研究了人脸检测的贝叶斯特征判别法，该方法包括三个部分：原始图像的特征判别分析、人脸区和其它区的统计建模以及贝叶斯分类器。
Moreover, the amount of examples needed to build a reliable classifier by statistical means is much larger than it is available for humans.
况且，通过统计手段来建立一个可靠的分类器，对于人类来说需要非常巨大的用例数目。
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.
在统计建模中，有很多分类器构建算法，每个算法构造一组不同的关于数据的假设集合。
We emphases discussed the nearest neighbor classifier and support vector machine (SVM) based on the statistical study theory.
在分类器的设计上，重点讨论了最近邻分类器和基于统计学习理论的支持向量机(SVM)。
Extract 21-dimensional statistical characteristics of histogram characteristic function (HCF) field, training classifier with the support vector machine (SVM).
提取直方图特征函数(HCF)域21维统计矩特征组成特征向量，用支持向量机(SVM)训练分类器。
To address the above problems of statistical classification methods, we propose a novel rule based classifier for railway transport information.
针对基于统计的分类方法的上述不足，本文进一步提出了新的基于规则的铁路运输信息数据分类方法。
The classifier is the key of the designing of the statistical classification method.
分类器的设计是统计分类方法的关键。
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.
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 statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.
为了获得最好的模型性能，挑选做出最合适假设的建模算法—而不只是选择你最熟悉那个算法，是很重要的。
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.
为了获得最好的模型性能，挑选做出最合适假设的建模算法—而不只是选择你最熟悉那个算法，是很重要的。
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