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Improving the efficiency of Knowledge Base Refinement

机译：提高知识库优化的效率

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摘要

This paper reports the STALKER knowledge base refinement system. Like its predecessor KRUST, STALKER proposes many alternative refinements to correct each wrongly classified example in the training set. Two enhancements have been made. Firstly, the class of errors handled has been augmented by the introduction of inductive refinement operators. Secondly, the testing phase has been greatly speeded up by using a Truth Maintenance System. The resulting system is more effective than other refinement systems because it generates many alternative refinements. At the same time, STALKER is very efficient since KRUST's computationally expensive implementation and testing of refined knowledge bases has been replaced by a TMS-based simulator.
机译：本文报告了STALKER知识库优化系统。像其前身KRUST一样，STALKER提出了许多替代性改进方案，以纠正训练集中每个错误分类的示例。进行了两项增强。首先，通过引入归纳精化运算符来扩大处理的错误类别。其次，使用真相维护系统大大加快了测试阶段。生成的系统比其他优化系统更有效，因为它会生成许多替代性优化。同时，STALKER非常高效，因为KRUST的计算昂贵的实现和对精炼知识库的测试已被基于TMS的模拟器所取代。

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  • 来源
    《Machine learning》|1996年|78-86|共9页
  • 会议地点 Bari(IT);Bari(IT)
  • 作者

    Leonardo Carbonara; Derek Sleeman;

  • 作者单位

    Department of Computing Science University of Aberdeen King's College Aberdeen AB9 2UE Scotland, UK;

    Department of Computing Science University of Aberdeen King's College Aberdeen AB9 2UE Scotland, UK;

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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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