Computationally Efficient Estimators for the Bayes Risk

Files in this item

Files Size Format View
Wil1978May9Computati.PDF 3.214Mb application/pdf Thumbnail

Show full item record

Item Metadata

Title: Computationally Efficient Estimators for the Bayes Risk
Author: Wilcox, Lynn D.; de Figueiredo, Rui J.P.
Type: Tech Report
Keywords: pattern recognition; Bayes Risk; error estimation
Citation: L. D. Wilcox and R. J. de Figueiredo, "Computationally Efficient Estimators for the Bayes Risk," Rice University ECE Technical Report, no. 7804, 1978.
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.
Date Published: 1978-05-20

This item appears in the following Collection(s)

  • ECE Publications [1046 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students