数据挖掘概念学习:Candidate-Eliminate算法流程图

本文介绍了Candidate-Eliminate算法的基本原理及实现过程。该算法通过处理正例和负例来逐步缩小假设空间,最终收敛到与训练集一致的假设集合。

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Candidate-Eliminate算法文字描述:

Initialize G to the set of most-general hypotheses in H Initialize S to the set of most-specific hypotheses in H For each training example, d, do: If d is a positive example then: Remove from G any hypotheses that do not match d For each hypothesis s in S that does not match d Remove s from S Add to S all minimal generalizations, h, of s such that: 1) h matches d 2) some member of G is more general than h Remove from S any h that is more general than another hypothesis in S If d is a negative example then: Remove from S any hypotheses that match d For each hypothesis g in G that matches d Remove g from G Add to G all minimal specializations, h, of g such that: 1) h does not match d 2) some member of S is more specific than h Remove from G any h that is more specific than another hypothesis in G

流程图:

算法的代码见下一篇博文!

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