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Knowledge acquisition by encoding expert rules versus computer induction from examples: A case study involving soybean pathology

机译:通过对专家规则进行编码来获取知识,而不是通过示例进行计算机归纳:涉及大豆病理学的案例研究

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摘要

In view of growing interest in the development of knowledge-based computer consulting systems for various problem domains, the problems of knowledge acquisition have special significance. Current methods of knowledge acquisition rely entirely on the direct representation of knowledge of experts, which usually is a very time and effort consuming task. The paper presents results from an experiment to compare the above method of knowledge acquisition with a method based on inductive learning from examples. The comparison was done in the context of developing rules for soybean disease diagnosis and has demonstrated an advantage of the inductively derived rules in performing a testing task (which involved diagnosing a few hundred cases of soybean diseases).
机译:鉴于人们越来越关注针对各种问题领域的基于知识的计算机咨询系统的开发,知识获取的问题具有特殊的意义。当前的知识获取方法完全依赖于专家知识的直接表示,这通常是非常耗时和费力的任务。本文介绍了将上述知识获取方法与基于归纳学习示例的方法进行比较的实验结果。比较是在制定大豆疾病诊断规则的背景下进行的,并且证明了归纳派生规则在执行测试任务(涉及诊断数百例大豆疾病)中的优势。

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