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.
统计结果表明，决策树分类可以获得更高的分类精度。
Results show that the decision tree classifier can achieve higher classification accuracy.
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.
本文提出MSS数据波谱形态相关模型和直方图决策树分类法。
An MSS spectral form correlation model and a histogram decision tree classifier are presented.
在数据挖掘中存在多种算法，决策树分类算法是应用比较多的一种。
There are some various algorithms in data mining, and decision tree classification algorithm is the most popular one.
最后进行了分步决策树分类实验和与传统分类方法的精度对比分析。
In the end, decision tree classification experiments results and contrastive precision accuracy are obtained.
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...
实验结果表明D- S决策树分类算法能有效的对不确定数据进行分类。
This D-S decision tree is a new classification method adapted to the uncertain data.
根据水稻生长期的高光谱数据的光谱特征，设计了一个混合决策树分类算法。
According to the rice spectral features of hyperspectral image data acquired during the rice is growing, a hybrid decision tree classification algorithm dealing with the variety of rice is developed.
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.
通过对决策树分类算法的比较，本文采用C4.5决策树算法实现自学习模块。
Comparing with Decision Tree algorithms, this system chooses the C4.5 to realize the self-learning module.
ID 3算法是数据挖掘中经典的决策树分类算法，该算法具有抗噪声能力差的缺点。
ID3 algorithm is a classical algorithm in data mining, this algorithm has the worse ability to resist noise.
用于知识发现的大部分数据挖掘工具均采用规则发现和决策树分类技术来发现数据模式和规则。
Most data mining tools for knowledge discovery generally use rule discovery and decision tree technology to extract data patterns and rules.
大部分数据挖掘工具采用规则发现和决策树分类技术来发现数据模式和规则，其核心是归纳算法。
Most data mining tools use rule discovery and decision tree technology to extract data patterns and rules; its core is the inductive algorithm.
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.
同时详细的阐述了决策树分类算法，并对比较流行的决策树算法id3、C4.5等算法进行详细分析与比较。
Meanwhile it describes the decision tree classification algorithm in detail, analyzes the ID3, C4.5 and other prevalent decision tree algorithm.
另外，本文在相关章节对形式概念分析和聚类分析进行比较以及分析总结了基于概念格的分类和决策树分类法的异同。
Moreover, this thesis compares FCA with clustering analysis, and analyzes the similarities and differences of classification based on concept lattice and decision tree.
该文介绍了随机决策树分类模型及如何启发式选择随机决策树的深度及棵树，通过实验证明了该算法的有效性和高效性。
This paper introduces the classification model of random decision tree and how to heuristic selected the depth and the number, the experiment shows that the algorithm is effectiveness and efficiency.
然后分别使用决策树、分类关联规则等技术对视频属性数据库进行数据挖掘，提取出决策树分类规则集和分类关联规则集；
Then the decision tree and class association rules mining are used on the video attribute database to extract a decision tree classification rule set and a class association rule set respectively.
实验结果表明，D - S证据理论决策树分类算法能有效地对不确定数据进行分类，有较好的分类准确度，并能有效避免组合爆炸。
This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.
分类与预测分析是数据挖掘的主要技术手段之一，至今已在理论和方法上取得了丰硕的研究成果，决策树分类算法就是其中最典型的代表。
Classify and prediction is the main measures in Data Mining, which make great progress in theory and method till now, and Decision Tree arithmetic is the symbol.
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.
实验结果表明，系统所使用的轮廓线向量图像特征也能够较有效地应用于图像方向分类，而机器学习则能够有效地为之建立决策树分类模型。
Using teacher images and machine learning method, an image direction classification model is built as a decision tree. Test results argued the validity of this method.
第二种技术是分类（即分类树或决策树），用来创建一个实际的分支树来预测某个未知数据点的输出值。
The second was classification (also known as classification tree or decision tree), which can be used to create an actual branching tree to predict the output value of an unknown data point.
分类(也即分类树或决策树)是一种数据挖掘算法，为如何确定一个新的数据实例的输出创建逐步指导。
Classification (also known as classification trees or decision trees) is a data mining algorithm that creates a step-by-step guide for how to determine the output of a new data instance.
创建一个分类树(一个决策树)，并借此挖掘数据就可以确定这个人购买一辆新的M5的可能性有多大。
By creating a classification tree (a decision tree), the data can be mined to determine the likelihood of this person to buy a new M5.
在构造决策树的过程中，分离属性选择的标准直接影响分类的效果。
In the process of constructing a decision tree, the criteria of selecting partitional attributes will influence the efficiency of classification.
经实验证明，用该方法构造的决策树与传统的基于信息熵方法构造的决策树相比较，复杂性低，且能有效提高分类效果。
The experiments show that, compared with the entropy-based method, our method is simpler in the structure, and can improve the efficiency of classification.
至今已经提出了决策树的很多算法，通过分析已知的分类信息得到一个预测模型。
So far, there are many algorithms have been given and we can gain a prediction model by analyzed known catalog information.
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