搜索资源列表
C4.5算法
- 数据挖掘中的决策树C4.5算法的实现,用matlab实现-Data Mining Decision Tree Algorithm of C4.5, using Matlab to achieve
CART_iris
- matlab数据挖掘算法。实用cart决策树进行分类,可识别多类。decision tree algorithm, classification.-Matlab data mining algorithms. Practical cart decision tree classification, identification number category. Decision tree algorithm, the classification.
crossvalidate
- 基于决策树的n则交叉验证分类器 (决策树程序直接调用matlab中的) crossvalidate.m N则交叉验证程序,N可选 NDT.mat 含9个国际公认标准数据集,已做过标么处理,可直接使用 专业-n Based on Decision Tree is cross-validation classification (decision tree directly call the Matlab) cr ossvalidate.m N is cross-validation
jueceshuID3
- 基于决策树的matlab的源程序,供大家参考
C45_Sun
- 常用决策树算法C4.5的实现代码。利用matlab实现。
ID3
- 数据挖掘中的决策树ID3算法,matlab的,请大家
id3matlab
- Id3是最基础的决策树分类方法,是其他决策树分类方法的基础,这个是Id3分类方法的matlab 实现
CART
- 用matlab编写的CART数据挖掘决策树算法-using Matlab CART prepared by the Data Mining Decision Tree Algorithm
GA_SVM
- 对于小样本而言,SVM的仿真效果要比神经网络好,但是SVM的性能依赖于它的两个训练参数,本算法是用GA自动选择SVM的两个参数。-For small sample case, SVM simulation results than the neural network is good, but the performance of SVM depends on its two training parameters, the algorithm is automatically selected
CART
- CART算法 经典的matlab实现 大家可以好好学习一下 有做决策树的可以和我交流-CART algorithm to achieve the classic matlab
id3-jueceshu
- 在matlab中实现ID3决策树算法--matlab源码-In the ID3 decision tree algorithm implemented in matlab- matlab source
C4_5
- C4.5决策树源代码,直接是matlab源代码-C4.5 decision tree source code matlab source code is directly
Machine Learning with TensorFlow
- 机器学习 人工智能 决策树 随机森林 机器学习 人工智能 决策树 随机森林(machine learning tensor flow intelligent artifish)
RandomForest_matlab
- 随机森林是一个包含多个决策树的分类器,通过matlab实现实现随机森林算法(Matlab implementation of random forest algorithm)
随机森林算法分类、回归
- 随机森林分类器,matlab写的,直接可以运行,不需要该任何东西,详细看readme和案例。-Random Forest classifier, matlab write, direct run, does not require that anything
Classifiers
- 我们需要成百上千的分类器来解决现实世界的分类吗 我们评估179分类17种分类器(判别分析,贝叶斯,神经网络,支持向量机,决策树,基于规则的分类器,升压、装袋、堆放、随机森林和其他合奏,广义线性模型,线性,偏最小二乘法和主成分回归,logistic回归、多项式回归、多元自适应回归样条等方法),实现在WEKA,R(有或没有插入包),C和Matlab,包括所有目前可用的相关分类。(Do-we-Need-Hundreds-of-Classifiers-to-Solve-Real-World-Class
adaboost
- 基于matlab平台的集成学习算法,基分类器为决策树的adaboost(An integrated learning algorithm based on MATLAB platform, the base classifier is AdaBoost of decision tre)
shengjing
- 神经网络BP,随机森林,决策树,遗传算法等 数苑炼金网课文件,详细代码注解(Neural Network BP, Random Forest, Decision Tree, Genetic Algorithms, etc. Course Documents of Shuyuan Alchemy Network, Detailed Code Annotations)
Random-Forests-Matlab-master (2)
- 要说随机森林,必须先讲决策树。决策树是一种基本的分类器,一般是将特征分为两类(决策树也可以用来回归,不过本文中暂且不表)。构建好的决策树呈树形结构,可以认为是if-then规则的集合,主要优点是模型具有可读性,分类速度快。(In machine learning, a random forest is a classifier that contains multiple decision trees, and its output category is determined by the m