A neural network architecture for generalized category perception
Brian B. Miller, Francis L. Merat · 2002
The recognition of objects given a complete or partial set of features is inherent in human intelligence. The fields of pattern recognition and artificial intelligence, among others, have addressed this topic with a variety of models which lack consistency and generality. Thus, it is the goal of this paper to set forth a generalized model for object recognition (classification). System models utilizing neural networks have been suggested for category perception. The proposed system is based on the principles of probability. We refer to this architecture as the generalized category perception model.>