搜索资源列表
c4.5.rar
- 通过C4.5 的实现可以进行构建决策树 来进行有效的分类,Through the realization of C4.5 decision tree can be constructed to carry out an effective classification
file
- c++ 实现 决策树 分类算法,规模很小但是很简练的一个算法-c++ realization of the decision tree classification algorithm, the size of a small but very concise algorithm for
mill
- 包含了很多分类算法,有SVM,knn,决策树等,还有文档说明-Contains a lot of classification algorithms, there is SVM, knn, decision tree and so on, have documented
1
- 论述了医学图像挖掘在计算机辅助诊断中的作用,提出了采用灰度级作为 CT 图像特征的思想、灰度级的提取和存储方法,介绍 了采用决策树分类算法和基于密度的聚类算法对胸部和头部 CT 图像进行分类和聚类的结果及其分析,给出了分析的结论和进一步的研究方向。-Image mining Computer-aided diagnoses Luminance grade Classification Clustering
id3matlab
- id3算法用MATLAB实现决策树分类器程序。-id3 decision tree!
C4_5
- c4.5经典算法,实现决策树分类功能,可以对连续数值和离散数值实现很好的分类,并有剪枝功能-c4.5 classic algorithms, to achieve the decision tree classification, can be continuous and discrete numerical values to achieve good classification, and a pruning function
Application_of_Decision_Trees_on_uncertain_data.ra
- 决策树分类算法在不确定型数据方面的应用,首先进行学习,然后进行分类-Application of Decision Trees on uncertain data
id
- 决策树Id3算法,基于决策树的分类算法,包含注释-fbjdhuieher
ID3
- ID3算法是数据挖掘中常用的算法,属于决策树分类算法,其分类速度较快,尤其对于大规模数据。-ID3 algorithm is commonly used in data mining algorithms, decision tree classification algorithm is, the classification speed, especially for large-scale data.
test_draworb0
- 高级信息提取 基于专家知识的决策树分类:规则获取(经验总结、数据挖掘如c4.5 cart算法)、规则定义以及构建决策树 -Advanced information extraction based on expert knowledge of the decision tree classification: the rules to get (lessons learned, data mining algorithms such as c4.5 cart), definit
test_object
- 高级信息提取 基于专家知识的决策树分类 -Advanced information extraction based on expert knowledge of the decision tree classification: the rules to get (lessons learned, data mining algorithms such as c4.5 cart), definitions and rules to build decision trees
chengxu
- 决策树分类,id3算法,数据挖掘,以学生成绩为例-Decision tree classification, id3 algorithm, data mining, to student achievement, for example
01决策树分类问题:预测销量高低
- 用python决策树分类问题:预测销量量的高低问题(Decision tree classification problem using Python: forecasting the volume of sales)
decision_tree
- 以隐形眼镜为例的决策树分类算法代码,lenses.data是存放数据的文件,此代码使用python3实现(The classification algorithm code of the decision tree with the contact lens as an example)
决策树java代码1
- 决策树可对数据进行分类,选出最大增益属性。(Decision tree can be classified data, select the maximum gain attribute.)
决策树与随机森林
- 给出对决策树与随机森林的认识。主要分析决策树的学习算法:信息增益和ID3、C4.5、CART树,然后给出随机森林。 决策树中,最重要的问题有3个: 1. 特征选择。即选择哪个特征作为某个节点的分类特征; 2. 特征值的选择。即选择好特征后怎么划分子树; 3. 决策树出现过拟合怎么办? 下面分别就以上问题对决策树给出解释。决策树往往是递归的选择最优特征,并根据该特征对训练数据进行分割。(The understanding of decision tree and random
chapter28
- 决策树分类器在乳腺癌诊断中的应用研究(2012b版本)(Application of decision tree classifier in breast cancer diagnosis (2012b version))
决策树-判断隐形眼镜的类型
- 使用python实现的利用随机数生成算法对一个实例,判断隐形眼镜类型的分类问题进行解决。(Use python and random decision tree algorithm to solve the classification problem)
决策树C4.5算法matlab源代码(完美运行)
- 可以完美的实现用于统计学习的算法C4.5分类,完整的matlab程序(Classification of algorithm C4.5 for statistical learning)
决策树分类实验(乳腺癌)
- 决策树分类程序,包含使用的数据集和运行结果(Decision tree classifier, including data sets used and running results)