An alternative method for inducing a membership function of a category

Abstract We proposed an alternative learning method for category classification knowledge. Our method induces a membership function for a category from positive and negative examples. It can learn “topological knowledge” such as typicality of an example. Our method consists of two stages: example space configuration of a coordinate system in the first stage, induction of membership function that induces a membership function based on the distance in the newly configured example space in the second. Further, we investigate search strategies suitable for deriving the symbolic expression of a category by geometric analysis of the example space.