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Description: Quinlan s C4.5 算法的实现-the implementation of C4.5
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Size: 2048 |
Author: g |
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Description: 数据挖掘算法，分类树的C4.5算法，用于模式分类-data mining algorithms, the C4.5 classification tree algorithm for pattern classification
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Size: 2048 |
Author: sah |
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Description: c4.5主要函数的matlab实现，简单易懂，扩展性很强-c4.5 main function of the Implementation of Matlab, simple, strong expansion
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Size: 2048 |
Author: 郝琳 |
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Description: ID3+C4.5的源程序。用于数据挖掘决策算法的一个实例。-ID3 C4.5 of the source. Data Mining for a decision algorithm examples.
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Size: 5120 |
Author: 张飞 |
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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.
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Size: 1024 |
Author: xinyuanwo |
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Description: 数据挖掘中的c4.5算法
给予决策树-Data Mining in the given decision tree algorithm c4.5
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Size: 3072 |
Author: gezn |
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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
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Size: 4096 |
Author: 凌风 |
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Description: C4.5算法 matlab实现的，感觉还可以，大家可以看看！-the implementation of C4.5 using matlab
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Size: 2048 |
Author: dizhaung |
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Description: C4.5 决策树算法源码。C4.5决策树是决策树领域的经典算法。以其为内容的书籍引用率已经达到一万次以上-C4.5 decision tree
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Size: 2048 |
Author: 赵小天 |
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Description: matlab源代码 C4.5决策树算法的matlab实现-C4.5
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Size: 2048 |
Author: 李军 |
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Description: c4.5 algorithm in matlab
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Size: 2048 |
Author: panshopr |
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Description: his algorithm was proposed by Quinlan (1993). The C4.5 algorithm generates a classification-decision tree for the given data-set by recursive partitioning of data. The decision is grown using Depth-first strategy. The algorithm considers all the possible tests that can split the data set and selects a test that gives the best information gain. For each discrete attribute, one test with outcomes as many as the number of distinct values of the attribute is considered. For each continuous attribute, binary tests involving every distinct values of the attribute are considered. In order to gather the entropy gain of all these binary tests efficiently, the training data set belonging to the node in consideration is sorted for the values of the continuous attribute and the entropy gains of the binary cut based on each distinct values are calculated in one scan of the sorted data. This process is repeated for each continuous attributes.
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Size: 2048 |
Author: rajesh |
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Description: 数据挖掘matlab源码,包含机器学习领域中经典算法如ID3,C4 5,NN,CARD,EM等(Data mining matlab source code, including the classical algorithms in the field of machine learning, such as ID3,C4 5 NNNCARDUM, etc.)
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Size: 665600 |
Author: vidao |
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