A CONNECTIONIST MODEL FOR CATEGORY PERCEPTION - THEORY AND IMPLEMENTATION

被引:17
作者
BASAK, J
MURTHY, CA
CHAUDHURY, S
MAJUMDER, DD
机构
[1] INDIAN STAT INST,COMMUN SCI UNIT,CALCUTTA 700035,INDIA
[2] INDIAN STAT INST,FGCS KBCS PROJECT,CALCUTTA 700035,INDIA
[3] INDIAN STAT INST,ECSU,NODAL CTR KNOWLEDGE BASED COMP,CALCUTTA 700035,INDIA
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 1993年 / 4卷 / 02期
关键词
D O I
10.1109/72.207613
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A connectionist model for learning and recognizing objects (or object classes) has been presented here. 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 has been 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 have been presented. The model can be applied for the detection of multiple objects occluding each other.
引用
收藏
页码:257 / 269
页数:13
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