{"title":"将多层属性图映射到识别网络","authors":"Hing-Yip Chan, D. Yeung, K.F. Cheung","doi":"10.1109/IJCNN.1991.170607","DOIUrl":null,"url":null,"abstract":"A methodology of synthesizing a neocognitron is presented. The goal is that the system parameters is a neocognitron can be 'programmed' rather than learned through laborious training. The tool used is the attribute graph theory. Using a set of attribute graphs describing structural and contextual information of different classes of patterns, one can synthesize a neocognitron through a mapping algorithm. The deformation-invariant attribute of the neocognitron can be preserved through the blurring of S-cells. The performance of the neocognitron obtained through the synthesis is contrasted with that of an identical neocognitron obtained through supervised training.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mapping multi-layer attributed graphs onto recognition network\",\"authors\":\"Hing-Yip Chan, D. Yeung, K.F. Cheung\",\"doi\":\"10.1109/IJCNN.1991.170607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A methodology of synthesizing a neocognitron is presented. The goal is that the system parameters is a neocognitron can be 'programmed' rather than learned through laborious training. The tool used is the attribute graph theory. Using a set of attribute graphs describing structural and contextual information of different classes of patterns, one can synthesize a neocognitron through a mapping algorithm. The deformation-invariant attribute of the neocognitron can be preserved through the blurring of S-cells. The performance of the neocognitron obtained through the synthesis is contrasted with that of an identical neocognitron obtained through supervised training.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170607\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A methodology of synthesizing a neocognitron is presented. The goal is that the system parameters is a neocognitron can be 'programmed' rather than learned through laborious training. The tool used is the attribute graph theory. Using a set of attribute graphs describing structural and contextual information of different classes of patterns, one can synthesize a neocognitron through a mapping algorithm. The deformation-invariant attribute of the neocognitron can be preserved through the blurring of S-cells. The performance of the neocognitron obtained through the synthesis is contrasted with that of an identical neocognitron obtained through supervised training.<>