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Bayes and pseudo-Bayes estimates of conditional probabilities and their reliability

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

23 Citations (Scopus)

Abstract

Various ways of estimating probabilities, mainly within the Bayesian framework, are discussed. Their relevance and application to machine learning is given, and their relative performance empirically evaluated. A method of accounting for noisy data is given and also applied. The reliability of estimates is measured by a significance measure, which is also empirically tested. We briefly discuss the use of likelihood ratio as a significance measure.
Original languageEnglish
Title of host publicationProceedings of the European Conference on Machine Learning (ECML-93)
PublisherSpringer
Pages136-152
Volume667
DOIs
Publication statusPublished - Apr 1993

Publication series

NameLecture Notes in Artificial Intelligence

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