{"title":"Fréchet Metric in Neural Network Theory","authors":"Renáta Masárová","doi":"10.2478/rput-2019-0032","DOIUrl":null,"url":null,"abstract":"Abstract This paper deals with application of a modified Fréchet metric to self-organizing neural networks, called Kohenen maps. The methodology used allows us to put more emphasis on the selected parameters in the input data. It can simplify finding the minimal distance dFj, since dFj∈ 〈0,1〉","PeriodicalId":21013,"journal":{"name":"Research Papers Faculty of Materials Science and Technology Slovak University of Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Papers Faculty of Materials Science and Technology Slovak University of Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/rput-2019-0032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract This paper deals with application of a modified Fréchet metric to self-organizing neural networks, called Kohenen maps. The methodology used allows us to put more emphasis on the selected parameters in the input data. It can simplify finding the minimal distance dFj, since dFj∈ 〈0,1〉
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