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Learning Despite Concept Variation by Finding Structure in Attribute-based Data

机译：通过在基于属性的数据中找到结构来学习概念差异

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摘要

Learning accuracy depends on concept variation. The accuracy of six learning systems (C4.5, Grove, Greedy, Fringe, LFC and MRP) is compared using a set of forty test concepts. The selection of these concepts was guided by the existence of structured concepts that appear in difficult real-world domains (such as protein folding). Such concepts often have embedded, implicit structure, which may be revealed through explicit relations. Experiments using these benchmark concepts show that concept variation affects systems that find relations, such as MRP, significantly less than the other learners. We analyze this distinctive behavior in terms of concept characteristics and relate it to system performance in real-world domains.
机译：学习准确性取决于概念的变化。使用一组40个测试概念比较了六个学习系统（C4.5，Grove，Greedy，Fringe，LFC和MRP）的准确性。这些概念的选择由存在于现实世界中困难领域（例如蛋白质折叠）中的结构化概念指导。这些概念通常具有嵌入式的，隐式的结构，可以通过显式关系来揭示它们。使用这些基准概念进行的实验表明，概念变化对发现关系的系统（例如MRP）的影响明显小于其他学习者。我们根据概念特征来分析这种独特的行为，并将其与现实世界中的系统性能相关联。

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  • 来源
    《Machine learning》|1996年|391-399|共9页
  • 会议地点 Bari(IT);Bari(IT)
  • 作者

    Eduardo Perez; Larry A. Rendell;

  • 作者单位

    Beckman Institute and Dept. of Computer Science University of Illinois at Urbana-Champaign 405 N. Mathews Avenue, Urbana, IL 61801;

    Beckman Institute and Dept. of Computer Science University of Illinois at Urbana-Champaign 405 N. Mathews Avenue, Urbana, IL 61801;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机的应用;
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