{"title":"通过竞争学习对票据货币进行分类","authors":"T. Kosaka, S. Omatu","doi":"10.1109/SMCIA.1999.782699","DOIUrl":null,"url":null,"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.","PeriodicalId":222278,"journal":{"name":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Bill money classification by competitive learning\",\"authors\":\"T. Kosaka, S. Omatu\",\"doi\":\"10.1109/SMCIA.1999.782699\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":222278,\"journal\":{\"name\":\"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMCIA.1999.782699\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMCIA.1999.782699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.