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MLC21NT-C
- machine learning, accuracy estimation, cross-validation, bootstrap, ID3, decision trees, decision graphs, naive-bayes, decision tables, majority, induction algorithms, classifiers, categorizers, general logic diagrams, instance-based algorit
decision-trees
- 决策树lisp代码 决策树lisp代码-decision tree lisp code decisio n tree lisp code
icsiboost-0.3.tar
- Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the
C50
- 功能强大的决策树分类算法，是C4.5的改进版本，但在精度，速度和内存开销上均有了很大的改进。目前由rulequest公司管理，其可执行程序版本为商业版本，此GPL许可的源代码对外发布。-Both C4.5 and C5.0 can produce classifiers expressed either as decision trees or rulesets. In many applications, rulesets are preferred because they are simp
NeC45
- NeC4.5 is a variant of C4.5 decision tree, which could generate decision trees more accurate than standard C4.5 decision trees, through regarding a neural network ensemble as a pre-process of C4.5 decision tree.
InductionofDecisionTrees
- 模式识别中多类分类问题决策树间接Induction of Decision Trees-Pattern Recognition in many types of decision tree classification of indirect Induction of Decision Trees
IDE
- The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.101
ID3
- The algorithm ID3 (Quinlan) uses the method top-down induction of decision trees. Given a set of classified examples a decision tree is induced, biased by the information gain measure, which heuristically leads to small trees. The examples are given
trees
- 树的和 ★问题描述 LISP是一种高级语言，可以用它来表示其它的数据结构，例如二叉树。二叉树由LISP的S 表达式来表示，其规则如下： 空树=() 树= (整数 左子树 右子树) 叶节点=(整数 () ()) ★编程任务 问题是给定一棵由LISP的S表达式决定的二叉树，问是否存在一条从根到叶节点的路径 P，使这条路径上所有节点的和等于一个给定的整数I。 ★数据输入 由文件input.txt给出输入数据。每个输入数据包含多组数据，每组数据包含一个整数I和
decision-trees_lisp
- 决策树,Machine Learning, Tom Mitchell, McGraw Hill,第3章决策树源码-decision-trees
dTREE
- 经典的数据挖掘算例的代码，生成决策树，可看到生成树的规则。-Classic example of data mining code, to generate decision trees can be seen spanning tree rules.
DecisionTrees
- 决策树的C/C++源码实现, 机器学习的代码-Decision Trees implementation in C/C++, Machine Learning Code
tree_engine.cpp
- The program using C++ and OpenCV library demonstrates how to use different decision trees
decisiontreeID3
- id3 program in java for decision trees
A-comparative-study-on-heuristic-algorithms-for-g
- A comparative study on heuristic algorithms for generating fuzzy decision trees
Ilija-Zdravkov---Decision-trees
- Visual C# desktop application for decision trees.
Decision-Tree
- 决策树是数据挖掘分类算法的一个重要方法。在各种分类算法中，决策树是最直观的一种。决策树(Decision Tree）是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率。-A decision tree is an important method of data mining classification algorithms. In various classification algorithms, decision trees is the most in
decision-tree
- 使用matlab建立决策树，分析数据。决策过程中使用了matlab自带的建立决策树的函数。-Using decision trees to predict contact lens type.Training data file (lense.txt) and descr iption file (comments.txt).
lecture-04-decision-trees-done
- Implementation of decision tree in data mining to solve multi branch problem
Decision-Trees
- Machine Learning Decision Trees