搜索资源列表
vc++c4.5
- 基于vc的决策树的分类算法是ID3算法的演变-vc on the decision tree classification algorithm is the evolution ID3 algorithm
c4.5数据挖掘算法源代码，LINUX版本
- 本程序是用c语言编写的基于决策树分类方法的数据挖掘算法，它对测试集进行分类，挖掘出潜在的规则-this procedure is used to prepare the language c decision tree classification based on the data mining algorithm, which tests set for classification, tapping the potential rules
lab
- 基于2叉树svm的入侵检测算法，构造偏态二叉-2-tree SVM-based intrusion detection algorithms, binary structure skewness
ChinesePronominalCoreferenceResolution
- 基于决策树的汉语代词共指消解 提出一种统计与规则相结合的决策树算法进行汉语代词共指消解 ,利用规则过滤掉属性冲突的反例 ,一定程 度上弥补了决策树算法忽略属性关联性的缺点. 采用 Chinese Treebank 作为语料进行测试 ,手工标注其中的共指 关系和特征向量 首先用规则过滤 ,然后采用 C415 决策树算法选择先行语. 实验结果显示 ,消解成功率为 82159 ,其中人称代词和指示代词的成功率分别为 87160 和 75121 .-A total based on de
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
DecisionTree
- 是基于c#开发环境实现的决策树算法。该算法是数据挖掘人工智能的非常重要的算法。-C# development environment is based on decision tree algorithm implementation. The algorithm is very important to artificial intelligence data mining algorithms.
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
machine-learningPPT
- 书中主要涵盖了目前机器学习中各种最实用的理论和算法，包括概念学习、决策树、神经网络、贝叶斯学习、基于实例的学习、遗传算法、规则学习、基于解释的学习和增强学习等-The book mainly covers the current variety of the most practical machine learning theory and algorithms, including the concept of learning, decision trees, neural network
C4.5
- 基于C4.5的决策树算法，一种非线性分类器-Based on C4.5 decision tree algorithm, a nonlinear classifier
new
- 机器学习算法决策树是基于机器学习的数据挖掘技术,它形式简单,分类速度快,无需先验知识,对样本分布也无要求且由决策树表达的规则直观清晰。-machine learning decision Tree
decision-tree
- 基于决策树的增量学习算法相关文档，主要用于文本分类-Incremental learning algorithm based on decision tree document, mainly for text classification
DecisionTree
- 决策树的经典C4.5算法，基于VS2010，对学习人工智能的同学有帮助-C4.5 decision tree algorithm, based on VS2010
tree-algorithm
- 基于ID3理论的二叉树决策树算法的C++实现-Implemented decision tree algorithm, based on the the ID3 theory of binary tree C++
decision-tree
- 关于决策树C4.5算法的几篇学术论文（基于C4.5算法的遥感影像分类）-Several papers (C4.5 decision tree algorithm C4.5 algorithm based on image classification)
c4.5
- C4.5是机器学习算法中的另一个分类决策树算法,它是基于ID3算法进行改进后的一种重要算法,相比于ID3算法,改进有如下几个要点:用信息增益率来选择属性.-C4.5 decision tree algorithm is another classification machine learning algorithm, which is based on ID3 algorithm is an important algorithm improved, compared to the ID3 a
adaboost
- 这个代码实现了基于树的adaboost算法，在一些简单数据集上表现很好。-This code implements tree based adaboost algorithm, performing well in some simple datasets.
several-classification-algorithm
- 几种基于Matlab的分类算法研究（自组织神经网络，SOM神经网络，LVQ神经网络，决策树，随机森林算法）-Several classification algorithm based on Matlab research (self-organizing neural network, SOM neural network and LVQ neural network, decision tree, the random forest algorithm)
dpsign-integrate
- 基于数据挖掘的决策树改进算法和贝叶斯改进算法,很有用的()
MLInActionCode-master
- 机器学习实战的源代码集合，第一部分主要介绍机器学习基础，以及如何利用算法进行分类，并逐步介绍了多种经典的监督学习算法，如k近邻算法、朴素贝叶斯算法、Logistic回归算法、支持向量机、AdaBoost集成方法、基于树的回归算法和分类回归树（CART）算法等。第三部分则重点介绍无监督学习及其一些主要算法：k均值聚类算法、Apriori算法、FP-Growth算法。第四部分介绍了机器学习算法的一些附属工具(Machine learning combat source code collection
决策树
- 决策树算法基于python语言的具体实现实例(Implementation of decision tree algorithm based on Python language)