{"title":"Probability limit property for energy function to feed-forward neural networks with noise","authors":"Cong Jin","doi":"10.1109/ICMLC.2002.1176695","DOIUrl":null,"url":null,"abstract":"A probability limit property is proposed for the weight vectors W of feed-forward neural networks when both the input data and output data contain noise or when only the output data contains noise. By theoretical analysis of the energy function of a feed-forward neural network, the paper points out that a least square energy function isn't a good choice. The result is good enough for future research.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"6 1","pages":"1-3 vol.1"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1176695","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
A probability limit property is proposed for the weight vectors W of feed-forward neural networks when both the input data and output data contain noise or when only the output data contains noise. By theoretical analysis of the energy function of a feed-forward neural network, the paper points out that a least square energy function isn't a good choice. The result is good enough for future research.
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