A Self-Organizing Neural Structure for Concept Formation from Incomplete Observation

We propose a self-organizing neural structure with dynamic and spatial changing weights for a feature space representation of concept formation. An essential core of this self-organization is based on an unsupervised learning with incomplete information for the dynamic changing and an extended Hebbian rule for the spatial changing. A concept formation problem requires the neural network to acquire the complete feature space structure of a concept information using an incomplete observation of the concept. The connection structure of self-organizing network can store with the information structure by using the two rules. The Hebbian rule can create a necessary connection corresponding to a feature space substructure of the complete information. On the other hand, unsupervised learning can delete unnecessary connections. Finally concept formation ability of the proposed neural network is proven under some conditions.