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

AbsfructWe 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 complere feature space strncture of a concept information using an incomplere observation of the concept. The connection structure of self-organizing network can store with the information structure by using the two rules. The Ilebhian rule can create a necessary connection corresponding to a feature space subsrruclure 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.