多重极小一般普化

Multiple Minimum General Generalization

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 ｍ ｕｌｔｉｐｌｅ ｍ ｉｎｉｍ ｕｍ ｇｅｎｅｒａｌｇｅｎｅｒａｌｉｚａｔｉｏｎ，简称ＣＭＧＧ）．该算法能够有效地产生多重极小一般普化。

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.