Title :
A connectionist model for category perception: theory and implementation
Author :
Basak, Jayanta ; Murthy, C.A. ; Chaudhury, Santanu ; Majumder, Dwijesh Dutta
Author_Institution :
Nat. Center for Knowledge Based Comput. Electron., Indian Stat. Inst., Calcutta, India
fDate :
3/1/1993 12:00:00 AM
Abstract :
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
Keywords :
image recognition; learning (artificial intelligence); neural nets; probability; category perception; connectionist model; error function; image recognition; learning system; overlapped feature train; probability; supervised mode; Abstracts; Artificial intelligence; Biological neural networks; Humans; Multilayer perceptrons; Object detection; Pattern recognition; Problem-solving; Resonance; Supervised learning;
Journal_Title :
Neural Networks, IEEE Transactions on
