decision tree

To improve the shortcomings of web-based learning system, a new model of the personalized learning system is presented based on data mining, and how to use the decision tree and the BP-neural network to design the personalized navigation module detailedly are described.

为改善基于Web学习系统存在的不足，提出了一个基于数据挖掘技术的个性化学习系统模型，并详细描述了应用决策树及BP神经网络算法对个性化导航模块设计的方法。

And it introduces some algorithms of decision tree learning such as ID3, C4.5 and feature subset selection of Inductive Learning.

介绍了归纳学习中的决策树学习算法如ID3、C4.5和特征子集选择问题。

After some typical decision tree arithmetic are discussed, the paper puts forward a metric-based decision tree.

在讨论了几种经典的决策树算法后, 论文提出了一种基于度量的决策树.

The system uses fuzzy Bayesian algorithm, decision tree, neural network, fuzzy detection.

该系统使用模糊算法中的贝叶斯，决策树，神经网络进行模糊检测。

Decision tree is a useful method of classification.

摘要决策树是分类的常用方法.

Then, the paper emphasizes the theory and method of Decision Tree algorithm and realizes C4. 5 algorithm.

随后，着重分析了决策树算法的理论背景以及实现步骤，并给出了C4.5算法的伪码实现。

Having no restriction of main memory and record number and attribute number of training database, the algorithm can output practice and accurate decision tree.

该算法没有训练数据库需存储于内存的限制，对训练库也没有记录个数及属性个数的限制，能生成简练、精确的决策树。

The proposed pruning stragey reduced computational complexity of the decision tree.

提出的剪枝方案降低了决策树的计算复杂度.

A Decision Tree Based Approach for Construction of Speech Database and Unit Selection

一种基于决策树模型的音库构建和基元选取方法

Application of Decision-tree in Analysis of Financial Report

决策树在财务报表分析中的应用

It presents data mining research in the synthesis database of microporous crystals built up by the State Key Laboratory of Inorganic Synthesis and Preparative Chemistry of Jilin University through query and decision tree.

采用数据挖掘中的查询和决策树技术对吉林大学无机合成与制备化学国家重点实验室建立的微孔晶体合成反应数据库进行了数据挖掘研究。

With respect to different attribute types, special methods are designed for int type, flags type and string type. Experiments to compare the decision tree matching method and the sequencial matching method are done.

针对网络入侵检测中所涉及到的不同数据类型，分别设计了整型字段、flags型字段以及字符型字段的匹配决策树节点的数据结构和匹配方法。

And lastly, the decision tree is produced using C4 5 algorithm.

最后用C45算法生成了决策树。

Application and Research of Decision Tree in Data Warehouse's ETL Process

一种决策树算法在数据仓库ETL中的应用与研究

A decision tree is a graphic model of a decision process.

决策树是描述决策过程的一种图形.

Two methods for text categorization fuzzy rule extraction are presented based on fuzzy decision tree.

本文提出了两种基于模糊决策树的模糊文本分类规则抽取方法.

Decision tree is a method to induce rules from data.

决策树是一种从数据中归纳规则的方法.

Evolutionary decision tree method has the advantage of global search.

演化决策树方法将传统的决策树算法与演化算法相结合,具有全局搜索的优点.

Two mining algorithms are used, Decision Tree and Naive Bayes, to build the predictive model from a dataset provided by UCI Machine learning and repository for machine learning and intelligence system.

同时选择了两个数据挖掘算法，分别是决策树和朴素贝叶斯算法，利用机器学习和智能系统的UCI机器学习资源库提供的数据集建立预测模型。

Experiments result proves that decision tree classifier is a effective classify method.

实验结果表明决策树分类器确实是一种有效的分类技术.

Finally, the decision tree method is used to accelerate the matching speed.

最后使用决策树方法提高匹配速度.

Among these methods, Decision Tree is one of the most universal models adopted.

在这些方法中, 决策树是采用最广泛的模型之一.

Research on the application of decision tree in desertification classification

决策树方法在土地荒漠化分类中的应用研究

Integrating the decision tree method, the classification of multi-classes was realized.

并与决策树方法相结合, 实现多类分类.

C 4.5 decision tree classifier, using the C language. U.S. with reference.

决策树C4.5分类器, 用C语言写的. 大家一起参考一下.

This paper studies on the decision tree algorithm and its application of market segmentation.

本论文主要研究决策树的算法及其在市场细分中应用.

Use Decision Tree Technology to Explore Gender Differences in Mobile Learning

决策树技术在移动学习性别差异研究中的应用

ID 3 is the key algorithm of decision tree algorithm, but there is existing variety bias problem.

ID3算法是决策树算法中的经典算法, 但存在多值偏向问题.

Then this paper points out the defect of traditional financial warning methods and designs a united data mining system consisting of Logistic regression, associative rules, decision tree and neural network.

针对这些局限性，设计了由Logistic回归分析方法、关联规则、决策树和神经网络组成的联合数据挖掘系统。

At the last, the paper gives experiment data and experiment result for improved decision tree.

最后, 论文给出了改进了的决策树的测试数据和测试结果.