计算学习理论应用于离散时间细胞神经网络

Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) Pub Date : 1994-12-18 DOI:10.1109/CNNA.1994.381691

W. Utschick, J. Nossek

{"title":"计算学习理论应用于离散时间细胞神经网络","authors":"W. Utschick, J. Nossek","doi":"10.1109/CNNA.1994.381691","DOIUrl":null,"url":null,"abstract":"The theory of probably approximately correct (PAC) learning is applied to discrete-time cellular neural networks (DTCNNS). The Vapnik-Chervonenkis dimension of DTCNN is determined. Considering two different operation modes of the network, an upper bound of the sample size for a reliable generalization of DTCNN architecture is given.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"47 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Computational learning theory applied to discrete-time cellular neural networks\",\"authors\":\"W. Utschick, J. Nossek\",\"doi\":\"10.1109/CNNA.1994.381691\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The theory of probably approximately correct (PAC) learning is applied to discrete-time cellular neural networks (DTCNNS). The Vapnik-Chervonenkis dimension of DTCNN is determined. Considering two different operation modes of the network, an upper bound of the sample size for a reliable generalization of DTCNN architecture is given.<<ETX>>\",\"PeriodicalId\":248898,\"journal\":{\"name\":\"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)\",\"volume\":\"47 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.1994.381691\",\"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 of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1994.381691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

将可能近似正确(PAC)学习理论应用于离散细胞神经网络(DTCNNS)。确定了DTCNN的Vapnik-Chervonenkis维数。考虑到网络的两种不同运行模式，给出了可靠推广DTCNN结构的样本容量上限。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Computational learning theory applied to discrete-time cellular neural networks

The theory of probably approximately correct (PAC) learning is applied to discrete-time cellular neural networks (DTCNNS). The Vapnik-Chervonenkis dimension of DTCNN is determined. Considering two different operation modes of the network, an upper bound of the sample size for a reliable generalization of DTCNN architecture is given.<>

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)

自引率

0.00%

发文量