DocumentCode :
3038860
Title :
Bill money classification by competitive learning
Author :
Kosaka, Toshihisa ; Omatu, Sigeru
Author_Institution :
Glory TD Himeji, Hyougo, Japan
fYear :
1999
fDate :
1999
Firstpage :
5
Lastpage :
9
Abstract :
The progress of computer science enables us to process complex and large scale computations and advanced pattern recognition methods can be adopted for pattern classification problems. Among them neuro-pattern recognition, which means pattern recognition based on neural networks, has been given attention since it has classified various patterns like human beings. We adopt the learning vector quantization (LVQ) method to classify money. The reasons for using the LVQ are that it can process unsupervised classification data and treat a large amount of input data with a small computational burden. We construct the LVQ network to classify Italian Lira. Compared with a conventional pattern matching technique, which has been adopted as a classification method, the proposed method has shown excellent classification results
Keywords :
financial data processing; neural nets; pattern classification; pattern matching; unsupervised learning; vector quantisation; Italian Lira classification; LVQ; competitive learning; learning vector quantization; money classification; neural networks; pattern classification; pattern matching; pattern recognition; unsupervised classification data; Biological neural networks; Computer science; Focusing; Humans; Large-scale systems; Pattern classification; Pattern matching; Pattern recognition; Pixel; Size measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Soft Computing Methods in Industrial Applications, 1999. SMCia/99. Proceedings of the 1999 IEEE Midnight-Sun Workshop on
Conference_Location :
Kuusamo
Print_ISBN :
0-7803-5280-7
Type :
conf
DOI :
10.1109/SMCIA.1999.782699
Filename :
782699
Link To Document :
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