| 引用本文: | 杨莉,胡守仁.一种具有知识评估和知识求精的知识获取(KER) 算法.[J].国防科技大学学报,1990,12(4):16-22.[点击复制] |
| Yang Li,Hu Shouren.KER: A Knowledge Acquisition Algorithm with Knowledge Evaluation and Knowledge Refinement[J].Journal of National University of Defense Technology,1990,12(4):16-22[点击复制] |
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| 一种具有知识评估和知识求精的知识获取(KER) 算法 |
| 杨莉, 胡守仁 |
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(计算机系)
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| 摘要: |
| 本文给出了一种获取多类知识的决策树算法，该算法根据所给定的属性的优先级和取值类型进行分类型知识的获取。为了保证获得知识的有效性，根据科恩(Cohen) 的归纳概率提出了一种证据支持程度来对所获得的知识进行评价，并相应地给出了一种知识求精的方法。 |
| 关键词: 人工智能，知识获取，算法/决策树，知识评估，知识求精，归纳概率 |
| DOI： |
| 投稿日期：1989-11-05 |
| 基金项目:国家自然科学基金资助 |
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| KER: A Knowledge Acquisition Algorithm with Knowledge Evaluation and Knowledge Refinement |
| Yang Li, Hu Shouren |
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(Department of Computer Science)
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| Abstract: |
| This paper gives a decision-tree algorithm acquiring many kinds of knowledge. The algorithm depends on the given priority and type of attributes to acquire the classified type of knowledge. In order to ensure the effectiveness of the acquired knowledge，a kind of evidence support degree to evaluate knowledge is proP9sed according to Cohen's inductive probability，and a kind of knowledge refinement method is given correspondingly. |
| Keywords: artificial intelligence,knowledge acquisition,algorithm/decision-tree,knowledge evaluation,knowledge refinement,inductive probability |
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