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web数据挖掘技术的决策树算法ID3的Java源代码。-web data mining technology ID3 decision tree algorithm Java source code. . . . . . .
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C4.5 算法是机器学习算法中的一种分类决策树算法,其核心算法是ID3算法. C4.5算法继承了ID3算法的优点,并在以下几方面对ID3算法进行了改进:
1) 用信息增益率来选择属性,克服了用信息增益选择属性时偏向选择取值多的属性的不足;
2) 在树构造过程中进行剪枝;
3) 能够完成对连续属性的离散化处理;
4) 能够对不完整数据进行处理。
C4.5算法有如下优点:产生的分类规则易于理解,准确率较高。其缺点是:在构造树的过程中,需要对数据集进行多次的顺序扫描
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c4.5分类器 支持向量机算法 文本分类 样本支持 核函数算法-c4.5 classifier SVM text classification algorithm sample support kernel function
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实现ID3 决策树算法,并使用MATLAB自带的工具箱函数画出决策树生成相应的规则-Achieve ID3 decision tree algorithm, and using MATLAB toolbox function that comes with the decision tree to generate the appropriate rules to draw
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Matlab语言实现的决策树ID3算法,分类功能强大,谁用谁知道-ID3 decision tree algorithm Matlab language, powerful classification, with who knows
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C4.5决策树算法java实现,未实现截枝部分-decision tree algorithm
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机器学习领域经典分类算法综述,包括Decision Tree(ID3、C4.5(C5.0)、CART、PUBLIC、SLIQ和SPRINT算法),三种典型贝叶斯分类器(朴素贝叶斯算法、TAN算法、贝叶斯网络分类器),k-近邻 、 基于数据库技术的分类算法( MIND算法、GAC-RDB算法),基于关联规则(CBA:Classification Based on Association Rule)的分类(Apriori算法),支持向量机分类,基于软计算的分类方法(粗糙集(rough set)、遗传
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决策树id3算法java实现,包含accesss数据-Id3 decision tree algorithm to achieve the java
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数据挖掘大作业aprior算法决策树算法-Data mining algorithms decision tree algorithm big job aprior
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单变量的决策树算法造成树的规模庞大,规则复杂,不易理解。本文结合粗糙集原理中的相对核及加权粗糙
度的方法,提出了一种新的多变量决策树算法。
-Decision Tree Algorithm in univariate tests caused large-scale, complex rules that are difficult to understand.
Based on the rough sets theory of attributes reduction, the c
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决策树算法J48的Java源程序,十大经典算法之一。eclipse环境下导入算法可进行数据分类。-J48 decision tree algorithm of Java source code, one of the top ten classical algorithm. eclipse environment can import data classification algorithms.
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决策树ID3算法的MATLAB程序,这里采用信息增益的方法.-MATLAB program of Decision Tree Algorithm ID3,by the information gain.
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采用Java实现的ID3决策树算法,基本实现了原理,支持分类,只支持标称型变量。-Java implementation using ID3 decision tree algorithm, the basic realization of the principle, support the classification, only support a nominal variable.
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数据挖掘中决策树生成算法,可以通过算法生成最小决策树,java实现-Data Mining Decision Tree algorithm can be generated by the smallest decision tree algorithm, java achieve
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决策树算法的实现,运用java语言,实现c4.5算法-Achieve decision tree algorithm, using java language, to achieve c4.5 algorithm
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C4.5是机器学习算法中的另一个分类决策树算法,它是基于ID3算法进行改进后的一种重要算法,相比于ID3算法,改进有如下几个要点:用信息增益率来选择属性.-C4.5 decision tree algorithm is another classification machine learning algorithm, which is based on ID3 algorithm is an important algorithm improved, compared to the ID3 a
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游戏AI介绍及决策树ID3算法的一个实现源码及思维过程。-Game AI introduction and ID3 decision tree algorithm source code and an implementation of the thinking process.
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C4.5决策树算法,C++算法,通过仔细阅读,可以很方便的了解跟学习决策树算法。-C4.5 decision tree algorithm, C++ algorithm, by reading carefully, you can easily understand the decision tree algorithm with learning.
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决策树算法c4.5 C语言实现以及命名规则和构建工具开发集合-C4.5 decision tree algorithm C language, naming and build tools set
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python3实现各种机器学习算法,包括knn算法,决策树算法,SVM算法,朴素贝叶斯算法等-Python3 realize all kinds of machine learning algorithms, including KNN algorithm, the decision tree algorithm, the SVM algorithm, naive bayesian algorithm, etc
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