신뢰도 추정을 위한 분산 학습 신경 회로망A variance Learning Neural Network for Confidence Estimation

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 465
  • Download : 5
DC FieldValueLanguage
dc.contributor.authorCho, Young B.-
dc.contributor.authorGweon, D. G.-
dc.date.accessioned2011-07-04T07:33:51Z-
dc.date.available2011-07-04T07:33:51Z-
dc.date.issued1997-06-
dc.identifier.citationJournal of the Korean Society for Precision Engineering, Vol.14, No.6en
dc.identifier.urihttp://hdl.handle.net/10203/24399-
dc.description.abstractMultilayer feedforward networks may be applied to identify the deterministic relationship between input and output data. when the results from the network require a high level of assurance, comsideration of the stochastic relationship between the input and output data may be very important.Variance is one of the effective parameters to deal with the stochastic relationship, This paper presents a new algorithm for a multilayer feedforward network to learn the variance of dispersed data without preliminary calculation of variance. In this paper, the network with this learning algorithm is named as a veriance learning neural network. Computer simulation examples are utilized for the demonstration and the evaluation of VANEAN.en
dc.language.isokoen
dc.publisherKorean Society for Precision Engineeringen
dc.subject확률과정en
dc.subject가우시안 분포en
dc.subject분산학습en
dc.subject신뢰도추정en
dc.subject다층신경회로망en
dc.subject다층퍼셉트론en
dc.title신뢰도 추정을 위한 분산 학습 신경 회로망en
dc.title.alternativeA variance Learning Neural Network for Confidence Estimationen
dc.typeArticleen
dc.subject.alternativeStochastic Processen
dc.subject.alternativeGaussian Distributionen
dc.subject.alternativeVariance Learningen
dc.subject.alternativeConfidence Estimationen
dc.subject.alternativeMultilayer Perceptronen
Appears in Collection
ME-Journal Papers(저널논문)

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0