| 摘要: |
| 文章提出一种广义θ-包含意义下的广义最小一般普化,称为多重极小一般普化.这一操作能够有效地减少普化程度,从而使过度普化问题较好地得以解决.为了有效地计算极小一般多重普化,文章研究了示例集上的普化范式与极小一般普化的关系,提出了一种基于概念聚类的归纳学习算法(clustering-based multiple minimum general generalization,简称CMGG).该算法能够有效地产生多重极小一般普化,并准确地反映出学习示例间的内在联系. |
| 关键词: 归纳学习,归纳逻辑程序设计,多重极小一般普化,最小一般普化. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家863高科技项目基金资助. |
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| Multiple Minimum General Generalization |
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YE Feng,XU Xiao-fei
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| Abstract: |
| In this paper, the authors present a kind of generalized least general generalization, called MGG (multiple minimum general generalization), under generalized θ-subsumption. MGG does effectively reduce the generalization of inductive hypotheses to extent, such that the problem of over-generalization is satisfactorily overcome. For computing MGG efficiently, the relation between normal generalization and MGG is studied and an algorithm CMGG (clustering-based multiple minimum general generalization) based on concept clustering is proposed, which can effectively figure out MGG and reflect accurately the internal relation of the set of learning examples. |
| Key words: Inductive learning, inductive logic programming, multiple minimum general generalization, least general generalization. |
