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Using the bottom clause and mode declarations in FOL theory revision from examples

机译:从示例中使用FOL理论修订中的bottom子句和模式声明

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摘要

Theory revision systems are designed to improve the accuracy of an initial theory, producing more accurate and comprehensible theories than purely inductive methods. Such systems search for points where examples are misclassified and modify them using revision operators. This includes trying to add antecedents to clauses usually following a top-down approach, considering all the literals of the knowledge base. Such an approach leads to a huge search space which dominates the cost of the revision process. ILP Mode Directed Inverse Entailment systems restrict the search for antecedents to the literals of the bottom clause. In this work the bottom clause and mode declarations are introduced in a first-order logic theory revision system aiming to improve the efficiency of the antecedent addition operation and, consequently, also of the whole revision process. Experimental results compared to revision system FORTE show that the revision process is on average 55 times faster, generating more comprehensible theories and still not significantly decreasing the accuracies obtained by the original revision process. Moreover, the results show that when the initial theory is approximately correct, it is more efficient to revise it than learn from scratch, obtaining significantly better accuracies. They also show that using the proposed theory revision system to induce theories from scratch is faster and generates more compact theories than when the theory is induced using a traditional ILP system, obtaining competitive accuracies.
机译:理论修订系统旨在提高初始理论的准确性,比纯粹归纳法产生更准确和可理解的理论。这样的系统搜索示例被错误分类的点,并使用修订运算符对其进行修改。这包括尝试按照自上而下的方法将子句添加到子句中,同时考虑知识库的所有文字。这样的方法导致巨大的搜索空间,这占据了修订过程的成本。 ILP模式定向逆蕴涵系统将对前件的搜索限制为bottom子句的文字。在这项工作中,在一阶逻辑理论修订系统中引入了底部子句和模式声明,目的是提高先行加法运算的效率,从而提高整个修订过程的效率。与修订系统FORTE进行比较的实验结果表明,修订过程平均快了55倍,产生了更易理解的理论,并且仍然没有显着降低原始修订过程所获得的准确性。此外,结果表明,当初始理论近似正确时,对其进行修改比从头开始学习更为有效,从而获得了更好的准确性。他们还表明,与使用传统ILP系统进行理论归纳时相比,使用拟议的理论修订系统从头开始归纳理论更快,并且生成的理论更紧凑,从而获得了竞争优势。

著录项

  • 来源
    《Machine Learning》 |2009年第1期|73-107|共35页
  • 作者单位

    Department of Systems Engineering and Computer Science-COPPE, Federal University of Rio de Janeiro (UFRJ), P.O. Box 68511, 21945-970 Rio de Janeiro, RJ, Brazil;

    Department of Systems Engineering and Computer Science-COPPE, Federal University of Rio de Janeiro (UFRJ), P.O. Box 68511, 21945-970 Rio de Janeiro, RJ, Brazil;

    Department of Systems Engineering and Computer Science-COPPE, Federal University of Rio de Janeiro (UFRJ), P.O. Box 68511, 21945-970 Rio de Janeiro, RJ, Brazil;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ILP; theory revision; mode directed inverse entailment (MDIE);

    机译:ILP;理论修订;模式有向逆蕴涵(MDIE);

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