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- Electro-acupuncture Jiaji acupoints treated lumbar disc herniation efficacy model analysis in clinical study(Abstract Objective: The aim of this study was to compare EA Jiaji acupoints therapy with EA conventional acupoints therapy,to validate their
C4_5
- matlab decision tree c4.5
trees
- 用PYTHON实现的决策树算法，简单明了，结构清晰(decision tree by python)
DicisionTree
- 决策树算法的简单实现,决策树（Decision Tree）是一种简单但是广泛使用的分类器。通过训练数据构建决策树，可以高效的对未知的数据进行分类。决策数有两大优点：1）决策树模型可以读性好，具有描述性，有助于人工分析；2）效率高，决策树只需要一次构建，反复使用，每一次预测的最大计算次数不超过决策树的深度。(A simple implementation of decision tree algorithms, decision tree (Decision Tree) is a simple b
dt_hw1
- CMU ML 课程作业第一次作业源代码，决策树分类算法(CMU ML homework code)
决策树（java）variance 回归
- mllib调用决策树的java代码，希望对大家有帮助(Mllib calls the Java code of the decision tree)
VFOI
- 用matlab编写的CART数据挖掘决策树算法 很好的 可以(Matlab prepared by the CART decision tree data mining algorithm is very good)
2012.李航.统计学习方法
- 《统计学习方法》是计算机及其应用领域的一门重要的学科。《统计学习方法》全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、k近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与最大熵模型、支持向量机、提升方法、EM算法、隐马尔可夫模型和条件随机场等。除第1章概论和最后一章总结外，每章介绍一种方法。叙述从具体问题或实例入手，由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。为满足读者进一步学习的需要，书中还介绍了一些相关研究，给出了少量习题，列出了主要参考文
决策树C4.5
- 利用决策树训练训练器 分辨 有毒蘑菇和无毒蘑菇 java书写，包含训练集及说明 代码无bug(Using decision tree training trainer to distinguish poisonous mushroom and non-toxic mushroom Java writing, including training set and explanation code.)
jueceshu
- 基于vc++的决策树算法，可以进行编译修改(Vc++ based decision tree algorithm, you can compile and modify)
algorithm
- In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes has been studied extensively since the 19
C4_5
- C4.5算法，优秀的决策树算法，由于求解特征分类问题(C4.5 algorithm, an excellent decision tree algorithm, especially for the problem of feature classification)
决策树资料合集
- 决策树源文件，实例，内容详解，word文档(Decision tree source file, instance)
决策树源代码合集
- 决策树，源代码，注释和详解，内附加说明，id3，CD_4型决策树(Decision tree, source code, notes and detailed explanation,)
类比法
- 型的类比学习方法是K-最近邻方法,它属于懒散学习法,相比决策树等急切学习法,具有训练时间短,但分类时间长的特点。K-最近邻算法可以用于分类和聚类中(The analogy learning method is K- nearest neighbor method. It belongs to the lazy learning method. Compared with the decision tree learning method, it has the characteristics o
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)
id3决策树
- 一个很好的关于决策树的算法matlab实现，由详细注释，易懂。(A good decision tree algorithm, matlab implementation, by detailed notes, easy to understand.)
ID3
- ID3决策树MATLAB代码，很详细，很实用(ID3 decision tree matlab code)
Chapter_2.1.1.3
- 贝叶斯算法、决策树、神经网络等算法的简单python实现(Bias algorithm, decision tree and neural network)
决策树
- 决策树id3算法matlab代码，已调试，根据需要改写main函数，实现数据分类功能(code for decision tree)