Computationally Efficient Estimators for the Bayes Risk
dc.citation.bibtexName | techreport | en_US |
dc.citation.issueNumber | 7804 | en_US |
dc.citation.journalTitle | Rice University ECE Technical Report | en_US |
dc.contributor.author | Wilcox, Lynn D. | en_US |
dc.contributor.author | de Figueiredo, Rui J.P. | en_US |
dc.date.accessioned | 2007-10-31T01:09:42Z | |
dc.date.available | 2007-10-31T01:09:42Z | |
dc.date.issued | 1978-05-20 | |
dc.date.modified | 2003-10-22 | en_US |
dc.date.submitted | 2003-10-22 | en_US |
dc.description | Tech Report | en_US |
dc.description.abstract | A 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.sponsorship | Air Force Office of Scientific Research | en_US |
dc.identifier.citation | L. 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.uri | https://hdl.handle.net/1911/20446 | |
dc.language.iso | eng | |
dc.subject | pattern recognition | * |
dc.subject | Bayes Risk | * |
dc.subject | error estimation | * |
dc.subject.keyword | pattern recognition | en_US |
dc.subject.keyword | Bayes Risk | en_US |
dc.subject.keyword | error estimation | en_US |
dc.title | Computationally Efficient Estimators for the Bayes Risk | en_US |
dc.type | Report | |
dc.type.dcmi | Text |
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