A Self-Organizing Model of Hyperinformation

Abstract

I propose a self-organizing model of hierarchical information. The hierarchical representation and learning algorithm are designed for the high-level information processing using neural networks. In this paper, I deal with both learning acqusition and representation issues in a single framework. The whole complex knowledge structure can be acquired through the piecewise relational information among hierarchical information. The self-organizing hierarchical information is implemented from an object-oriented programming paradigm, Inferences are proceeded by sending and receiving messages among information-objects. Since they are proceeded inside information objects, the inference processes are autonomous and parallel.