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.
|Title of host publication||Machine Learning: ECML-93|
|Editors||Pavel B. Brazdil|
|Number of pages||17|
|Publication status||Published - 1993|