Description: C4.5算法进行决策树生成 以信息增益最大的属性作为分类属性，生成决策树，从而得出决策规则。-C4.5 decision tree algorithms to generate information gain the greatest attribute as a classification attributes, generate decision tree, and came to decision-making rules. Platform: |
Size: 11264 |
Author:亚亚 |
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Description: 用c++实现的决策树算法，windows环境下，希望对学习数据结构和算法的朋友有所帮助。-achieve with the Decision Tree Algorithm, windows environment, and I hope to learn from data structures and algorithms friends help. Platform: |
Size: 149504 |
Author:yu |
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Description: 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 Platform: |
Size: 2048 |
Author:利军 |
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Description: C4.5是决策树的经典算法
C4.5 归纳学习是完全自动的学
习算法，所需要做的是选取有用的特征，构建实例数据库供它学习-C4.5 decision tree is the classic C4.5 inductive learning algorithm is completely automatic learning algorithm, what needs to be done is to select useful features, build databases for its examples of learning Platform: |
Size: 1024 |
Author:唐宇 |
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Description: 这是决策树C4.5算法的java版源码。希望大家能喜欢，愿共同分享！-Decision Tree Algorithm C4.5 java version of the source code. Hope you will like it is willing to share! Platform: |
Size: 1024 |
Author:gosiger |
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Description: 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 Platform: |
Size: 490496 |
Author:磨高尚 |
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Description: 以从医院病案室获得的3022例数据为样本，在完成样本数据库以及糖尿病并发症的多维数据集设计后，以糖尿病并发症流行病学知识发现为重点，研究定性数据定量化挖掘模型及算法引擎的设计与实现，即将关联模型引入糖尿病并发症的流行病学研究.运用决策树技术对数据样本进行分析，采用C4.5找到最优决策树-cases from the hospital to obtain the data for 3,022 cases samples the completion of the sample database and diabetic complications multidimensional data sets design, Complications of diabetes epidemiology knowledge discovery as the focus, Quantitative study of qualitative data mining engine model and algorithm design and implementation, Relational Model forthcoming introduction of diabetic complications epidemiological studies. the use of decision tree technical data samples for further analysis. C4.5 used to find optimal decision tree Platform: |
Size: 308224 |
Author:Eric Cheng |
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Description: 数据挖掘算法中，有关决策树算法c4.5的实现，c4.5是在ID3基础上实现的，有这比id3还好的功能-Data mining algorithms, the decision tree algorithm c4.5 realization, c4.5 is based on ID3, with this function better than id3 Platform: |
Size: 160768 |
Author:司马腾 |
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Description: 决策树是表达知识的一种有效形式，这个是挖掘决策树的经典c4.5算法-Decision tree is to express an effective form of knowledge, this is a classic mining decision tree algorithm c4.5 Platform: |
Size: 261120 |
Author:mameng |
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Description: 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和排序，因而导致算法的低效。此外，C4.5只适合于能够驻留于内存的数据集，当训练集大得无法在内存容纳时程序无法运行。-C4.5 algorithm has the following advantages: the classification rules easier to understand, accurate and a higher rate. Its shortcomings are as follows: in the tree structure, the need for a number of data sets the order of scanning and sorting, thus leading to inefficient algorithms. In addition, C4.5 can only be applied to the presence of a data set in memory, when the training set too great to accommodate in memory when the program can not run. Platform: |
Size: 1024 |
Author:xinyuanwo |
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Description: 机器学习中,很重要的一种技术是决策树,这是一个关于决策树技术的详细介绍-Machine learning, it is important and technology is a decision tree, this is a detail on the decision tree technology introduction Platform: |
Size: 1529856 |
Author:syscxl |
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Description: 通过C4.5 的实现可以进行构建决策树 来进行有效的分类-Through the realization of C4.5 decision tree can be constructed to carry out an effective classification Platform: |
Size: 538624 |
Author:shi |
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Description: 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.
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Size: 8192 |
Author: |
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Description: c4.5 关于决策树decision tree的matlab实现程序 -c4.5 decision tree decision tree on the realization of the matlab program Platform: |
Size: 4096 |
Author:凌风 |
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Description: 决策树算法C4.5源码,
The directory Data contains some sample datasets. Many interesting data
collections are maintained by the University of California, Irvine, in a
data repository-C4.5 decision tree algorithm source, The directory Data contains some sample datasets. Many interesting datacollections are maintained by the University of California, Irvine, in adata repository Platform: |
Size: 148480 |
Author:张耀 |
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Description: 决策树关联规则比较经典的算法，通常一下大型的应用项目使用，因为其稳定，实用！-Comparison of the classical association rules decision tree algorithm, usually about the use of large-scale application projects, because of its stability, and practical! Platform: |
Size: 160768 |
Author:李伟 |
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Description: C4.5 is an algorithm used to generate a decision tree. C4.5 is an extension of Quinlan s earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. Platform: |
Size: 2502656 |
Author:bwindhya |
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Search - decision tree c4.5