Description: 这是我从weka主页上下载的一个c4.5算法的改进算法,里面附有readme文件,只有算法的主体,大约600行代码-weka from the home page to download an algorithm Bank improved algorithm inside with the readme file, only the main algorithm, about 600 lines of code Platform: |
Size: 8347 |
Author:孙为 |
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Description: 这是我从weka主页上下载的一个c4.5算法的改进算法,里面附有readme文件,只有算法的主体,大约600行代码-weka from the home page to download an algorithm Bank improved algorithm inside with the readme file, only the main algorithm, about 600 lines of code Platform: |
Size: 8192 |
Author:孙为 |
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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: Now, you ought to implement the AdaBoost.M1 and AdaBoost.M2 algorithms. These algorithms
are two versions of the AdaBoost algorithm for handling the Problems with more than two
classes. You must first read the paper “Experiments with a New Boosting Algorithm”.
Use decision stump and C4.5 classifiers of Weka as the base classifiers for AdaBoost.M1 and use
decision stump as the base classifier for AdaBoost.M2. Platform: |
Size: 432128 |
Author:hajar |
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