Learning hierarchical concepts based on higher-order fuzzy semantic cell models through the feed-upward mechanism and the self-organizing strategy

摘要

Concept representation and learning is a basic topic of artificial intelligence. The aim of this paper is to explore the representation issue and the learning issue of abstract concepts. In this paper, we first introduce higher-order fuzzy semantic cell models to represent abstract concepts, based on which we develop a hierarchical representation of concepts called abstract concept graphs. Then, we put forward an unsupervised algorithm to learn a second-order abstract concept graph from a given data set. This method combines the feed-upward mechanism and the self-organizing strategy. In addition, we provide an evaluation metric for this learning algorithm. A series of experiments is provided to demonstrate the feasibility and validity of the proposed method. We also conduct a preliminary exploration of the potential application of this method to image segmentation.