Computationally Efficient Estimators for the Bayes Risk

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dc.citation.bibtexNametechreporten_US
dc.citation.issueNumber7804en_US
dc.citation.journalTitleRice University ECE Technical Reporten_US
dc.contributor.authorWilcox, Lynn D.en_US
dc.contributor.authorde Figueiredo, Rui J.P.en_US
dc.date.accessioned2007-10-31T01:09:42Z
dc.date.available2007-10-31T01:09:42Z
dc.date.issued1978-05-20
dc.date.modified2003-10-22en_US
dc.date.submitted2003-10-22en_US
dc.descriptionTech Reporten_US
dc.description.abstractA computationally efficient estimator for the Bayes risk is one which achieves a desired accuracy with a minimum of computation. In many problems, for example speech recognition, point evaluations of the class conditional densities are computationally costly. Density evaluations are the single most important factor contributing to the computational effort in Bayes risk estimation, thus the amount of computation required by a bayes risk estimator is defined as the average number of conditional density evaluations it performs. The accuracy of a risk estimator is defined by its variance.en_US
dc.description.sponsorshipAir Force Office of Scientific Researchen_US
dc.identifier.citationL. D. Wilcox and R. J. de Figueiredo, "Computationally Efficient Estimators for the Bayes Risk," <i>Rice University ECE Technical Report,</i> no. 7804, 1978.
dc.identifier.urihttps://hdl.handle.net/1911/20446
dc.language.isoeng
dc.subjectpattern recognition*
dc.subjectBayes Risk*
dc.subjecterror estimation*
dc.subject.keywordpattern recognitionen_US
dc.subject.keywordBayes Risken_US
dc.subject.keyworderror estimationen_US
dc.titleComputationally Efficient Estimators for the Bayes Risken_US
dc.typeReport
dc.type.dcmiText
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