Probabilistic Rule Learning
Probabilistic Rule Learning
复制标题
概率规则学习
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Ingo Thon
中科院分区：
文献类型：
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作者：
L. D. Raedt;Ingo Thon
传统上，规则学习器从确定性数据中学习确定性规则，也就是说，规则被表示为逻辑语句，并且示例及其分类也是纯逻辑的。我们将规则学习升级为概率设置，其中示例本身及其分类都可以是概率的。该设置被纳入概率规则学习器ProbFOIL中，该概率规则学习器ProbFOIL将关系规则学习器FOIL的原理与概率Prolog ProbLog相结合。我们还报告了一些实验，证明该方法的实用性。
Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade rule learning to a probabilistic setting, in which both the examples themselves as well as their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL, which combines the principles of the relational rule learner FOIL with the probabilistic Prolog, ProbLog. We report also on some experiments that demonstrate the utility of the approach.