搜索资源列表
Adaboost
- matlab实现AdaBoost,弱分类算法包括fisher 伪逆 svm naivebayes 决策树。-matlab implements AdaBoost, weak classification algorithms include fisher pseudoinverse svm naivebayes tree.
decision-tree
- 程序实现了生成决策树并使用决策树对离散特征向量进行分类。在判断是否要继续生成叶节点时采用entropy来作为impurity的measurement.-Program and use the decision tree decision tree to generate discrete feature vectors for classification. In determining whether to continue generating leaf nodes using entrop
C4_5
- 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. 分类决策树算法是从大量事例中进行提取分类规则的自上而下的决策树. -C4.5 algorithm is a machine learning algorithm, a classification decision tree algorithm, the core algorithm is ID3 algorithm classification tree algorithm is extracted from
DM6
- 如何使用matlab自带的决策树进行数据的分类，包括分类树，回归树-How to use matlab own decision tree classification of data, including classification trees, regression trees
DTree
- 一个实现分类决策树算法的系统。ID3算法和C4.5算法。-A decision tree algorithm to achieve classification system. ID3 and C4.5 algorithms.
DecisionTreeYSD
- 决策树的Matlab实现，实现了分类问题和回归问题，有很好的调试结果-Matlab implementation of decision trees
CART
- matlab中cart决策树源代码，用于数据的分类和预测。-source code in matlab cart decision tree for data classification and prediction.
AdaBoost
- 基于决策树实现的AdaBoost分类器，是一个由多个弱分类器进行训练生成强分类的过程。-Based on AdaBoost classifier tree implementation, is a plurality of weak classifiers in the training process generates a strong classifier.
RandomForest
- 基于决策树实现的随机森林算法，是数据挖掘中一个经典的分类算法，对初学者具有很好的学习参考意义。-Based on Decision Tree achieved random forests algorithm is a classical data mining classification algorithm, for beginners to learn with a good reference value.
DM
- 这个是数据挖掘关于加利福利亚州的小区房价的分析与预测，所用的分类方法有决策树和神经网络-This is a classification of data mining analysis and prediction about California' s residential housing prices, the use of decision trees and neural networks have
binarytree
- 自己做的数字识别，二叉决策树及1-v-1分类识别，有具体的注释说明，简单易懂，正确率高，可以直接运行！-digital identification designed by myself, binary decision tree and 1-v-1 classification, there are specific notes, easy to understand, good correct rate, you can directly run!
C4.5
- 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
randomForest_4.6-6.tar
- R软件下的randomforest实现，可以帮助实现对数据的分类和预测，将数据分为几类，然后再根据决策树做预测-R randomforest
ID3
- 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
Ch03
- 基于python的决策树的代码,包括树的构造，绘图，存储，分类。-Based on the decision tree python code, including the tree structure, graphics, storage, classification.
Trees-J48-Java-Code
- 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.
Tree-ID3-Java-Code
- 决策树之经典算法ID3的Java源代码，eclipse环境下进行数据分类必备算法之一。-Classic decision tree algorithm ID3 Java source code, one classification algorithms necessary data for the next eclipse environment.
C4.5
- 决策树C4.5 用于分类与预测的算法 简洁方便-decision tree C4.5 For classification and prediction algorithm Simple and convenient
CART
- CART分类树与回归树 Cart分类是决策树中的重要算法-Classification and regression tree
ID3
- 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.