首页> 外文会议>Proceedings of the 2007 International Conference on Machine Learning and Cybernetics >A CLAUSE LEARNING ALGORITHM COMBINING IMMUNE MECHANISM TO INVENT PREDICATE
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A CLAUSE LEARNING ALGORITHM COMBINING IMMUNE MECHANISM TO INVENT PREDICATE

机译:结合免疫机制进行预测的条款学习算法

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Aiming at the larger search space of learning clause in Inductive Logic Programming, we put forward the definitions of the predicate template and the clause template to reduce search space.We suggest IMPI algorithm, which is a genetic algorithm combining immune mechanism and uses the clause template as genetic code to learn the clause template of required clause.IMPI uses immune mechanism to invent new predicates, which can extend the hypothesis language, find better results.Correspondingly we design when to invent predicates.After obtaining the clause template, we use a general method based on generalization and information gain sampling to convert clause template to clause.We design the corresponding fitness function and genetic operator.It indicates that this algorithm can reduce the search space, improve the efficiency of search algorithm and can learn recursion clause by theoretical analysis and experiment comparison.It is an effective clause learning algorithm.
机译:针对归纳逻辑编程中学习子句的较大搜索空间,提出了谓词模板和子句模板的定义,以减少搜索空间。提出了IMPI算法,它是一种结合免疫机制的遗传算法,使用子句模板。作为遗传密码来学习所需子句的子句模板。IMPI利用免疫机制发明新的谓词,可以扩展假设语言,找到更好的结果。相应地,我们设计了何时发明谓词。泛化和信息增益采样的方法将子句模板转换为子句。我们设计了相应的适应度函数和遗传算子。表明该算法可以减少搜索空间,提高搜索算法的效率,并且可以通过理论分析来学习递归子句和实验比较。这是一种有效的从句学习算法。

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