A connectionist model for category perception: theory and implementation
Jayanta Kumar Basak, C.A. Murthy, Santanu Chaudhury, D. Dutta Majumder · IEEE Transactions on Neural Networks · 1993
A connectionist model for learning and recognizing objects (or object classes) is presented. The learning and recognition system uses confidence values for the presence of a feature. The network can recognize multiple objects simultaneously when the corresponding overlapped feature train is presented at the input. An error function is defined, and it is minimized for obtaining the optimal set of object classes. The model is capable of learning each individual object in the supervised mode. The theory of learning is developed based on some probabilistic measures. Experimental results are presented. The model can be applied for the detection of multiple objects occluding each other.