本文提出了一种基于决策树分类器的数据包分类算法。
The thesis researches an algorithm based on decision tree classifier for packet filtering.
方法：以决策树分类器为工具，用分类正确率衡量辨证一致性。
Methods: the decision tree classifier is used as a tool and the rate of classification accuracy is used to measure the consistency.
实验结果表明，这种方法构造的SVM决策树分类器分类性能较好。
The results of experiment demonstrate that the SVM decision tree built up by this method has a good classification performance.
实验结果表明，这种方法构造的SVM决策树分类器分类性能较好。
The results of experiment demonstrate that the SVM decision tree built up by...
把决策树分类器引入包过滤技术当中，改变了传统的顺序检索包过滤的方法。
Introducing the decision tree classifiers into packet filtering technology, it changes the conventional method searching the packet orderly.
其次，在解决工艺参数优化的问题中，本文提出了一种正演的方法，即结合使用决策树分类器以及人工神经网络进行综合分析的方法来完成。
Secondly, in solving the problem that the craft parameter is optimized, this paper has put forward a method to perform, which using decision tree and ANN carry on comprehensive.
利用决策树算法对乳腺癌图像数据进行分类，实现了一个基于决策树算法的医学图像分类器，获得了分类的实验结果。
Decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm, the experiment results are given.
决策树是归纳学习和数据挖掘的重要方法，通常用来形成分类器和预测模型。
Decision tree is an important method in induction learning as well as in data mining, which can be used to form classification and predictive model.
提出设计树和分类器设计器的概念，分别用于决策树设计过程的控制和决策树节点中的分类器的设计。
The idea of designing tree and classifier designer are proposed to control the design process of decision tree and de-sign the classifier of the node of decision tree.
经过研究，发现用决策树分类方法，在各节点设计不同的分类器，可以有效地提取山区中的水体。
The decision tree algorithm including several classifiers was put forward, which can extract water bodies effectively and easily.
为了证明算法的有效性，采用决策树作为基分类器。
In order to prove the validity of the algorithm, it USES decision tree as the base learner.
方法利用决策树算法对乳腺癌图像数据进行分类，提出了一个基于决策树算法的医学图像分类器。
Methods decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm.
并利用先验信息分类器分类效率高的优点，在一定程度上弥补了决策树错误累积的缺陷。
Besides, it takes advantage of the high efficiency of priori information classifier and, to a certain extent, makes uo for the deficiencies of the decision tree in error accumulation.
并利用先验信息分类器分类效率高的优点，在一定程度上弥补了决策树错误累积的缺陷。
Besides, it takes advantage of the high efficiency of priori information classifier and, to a certain extent, makes uo for the deficiencies of the decision tree in error accumulation.
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