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A knowledge-intensive approach to learning relational concepts

机译:学习关系概念的知识密集型方法

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摘要

We describe a new approach to integrating explanation-based and empirical learning methods for learning relational concepts. The approach uses an information-based heuristic to evaluate components of a hypothesis that are proposed either by explanation-based or empirical learning methods. Providing domain knowledge to the integrated system can decrease the amount of search required during learning and increase the accuracy of learned concepts, even when the domain knowledge is incorrect and incomplete.
机译:我们描述了一种新的方法来整合基于解释和经验的学习方法来学习关系概念。该方法使用基于信息的启发式方法来评估假设的组成部分,这些假设是通过基于解释的方法或基于经验的学习方法提出的。即使在领域知识不正确和不完整的情况下,向集成系统提供领域知识也可以减少学习过程中所需的搜索量并提高学习概念的准确性。

著录项

  • 来源
    《Machinee learning》|1991年|432-436|共5页
  • 会议地点 Evanston IL(US);Evanston IL(US)
  • 作者单位

    Department of Information and Computer Science University of California, Irvine Irvine, CA 92717 USA;

    Department of Information and Computer Science University of California, Irvine Irvine, CA 92717 USA;

    Department of Information and Computer Science University of California, Irvine Irvine, CA 92717 USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机的应用;
  • 关键词

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