搜索资源列表
Weka
- 使用weka api实现决策树代码，为了做毕业设计使用的。
C4.5_Source
- 使用纯JAVA语言原生开发C4.5决策树算法，没有使用工具包，并且通过界面展示了生成的决策树图像。-Using pure JAVA language Native Development C4.5 decision tree algorithm, do not use the toolkit, and by interface shows the image generated by the decision tree.
jbrt-master
- 机器学习算法中的迭代决策树算法，是一种用途非常广泛的多决策树算法，可以有效的解决问题-Machine learning algorithms in iterative decision tree algorithm, is a very versatile multi-tree algorithm can effectively solve the problem
source
- 对两个数据集分别建立决策树，并运用自适应算法和随机森林，并验证准确率-Of the two data sets were established decision tree, and the use of adaptive algorithms and random forest, and verify accuracy
ID3-java
- 这是模式识别中的决策树那一章中的ID3算法，包含很多文件-This is a pattern recognition decision tree ID3 algorithm of the chapter, contains many files
ID3java
- java语言实现决策树，采用传统的ID3算法实现-java language tree
src.tar
- c45算法java实现,机器学习决策树算法的简单实现-c45 algorithm to achieve the java machine learning decision tree algorithm simple implementation
ID3
- Java实现决策树方法 ID3算法的简单例子-Java ID3 decision tree algorithm is simple example
Decision_MakingTree
- java实现决策树算法,根据输入的样本集生成相应的决策树。-This is the code about Decision_MakingTree.
C4.5
- 数据挖掘 决策树算法 C4.5 JAVA编程实现 GUI界面显示决策树的分析结果-DataMining decision tree C4.5 in java code GUI interface to show the result of decision tree
ID3
- ID3决策树的建立，直接在eclipse上运行-ID3 decision tree establishment, run directly in the eclipse
RandomForest
- 随机森林是由多棵树组成的分类或回归方法。主要思想来源于Bagging算法，Bagging技术思想主要是给定一弱分类器及训练集，让该学习算法训练多轮，每轮的训练集由原始训练集中有放回的随机抽取，大小一般跟原始训练集相当，这样依次训练多个弱分类器，最终的分类由这些弱分类器组合，对于分类问题一般采用多数投票法，对于回归问题一般采用简单平均法。随机森林在bagging的基础上，每个弱分类器都是决策树，决策树的生成过程中中，在属性的选择上增加了依一定概率选择属性，在这些属性中选择最佳属性及分割点，传统做法
MinMaxTree
- 游戏树在人工智能的应用相当重要，若要寻找某赛局中最佳的步法的一个方式，是利用极小化极大算法在游戏树中搜寻最佳解，例如在井字游戏中电脑可以很快速地找到最佳解并做出决策。-Application of artificial intelligence in the game tree is very important, to find an innings in a manner best step is to use Minimax in the game tree search for the
DecisionTree
- 机器学习算法决策树算法中ID3算法的简单实现代码-Machine learning algorithms decision tree algorithm ID3 algorithm implementation code simple
weka
- 机器学习调用weka的jar包实现的源码，包含朴素贝叶斯,决策树,ID3,以及特征选择的源码，数据集使用weka的数据集，需要使用arff文件读入。-Weka machine learning to call the jar package implements the source, including Naive Bayes, decision trees, ID3, and features selected source dataset weka data set, you need t
Decision-tree-algorithm
- 数据挖掘，机器学习经典分类算法,决策树算法ID3 C4.5 Java实现 开发环境 eclipse-Data mining, machine learning classic classification algorithm, decision tree algorithm ID3 C4.5 Java development environment eclipse
findWebshell-master
- webshell后门检测脚本,基于决策树取得很高检测率(a scr ipt to find webshell in webserver)
C45-3
- 实现C4.53决策树算法，实现数据的分类(Implementation of C4.53 decision tree algorithm)
rseslib-3.0.4-src
- 包含很多知名算法实现，支持向量机，决策树，粗糙集，贝叶斯分类器等，适合学术研究，短评论意见挖掘，文本分类等(It includes many well-known algorithm implementation, support vector machine, decision tree, rough set, Bias classifier, etc., which is suitable for academic research, short comment mining, text c