Empirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on the performance of learning algorithms.
Read more about Empirical Risk Minimization: Background, Empirical Risk Minimization
Famous quotes containing the words empirical and/or risk:
“To develop an empiricist account of science is to depict it as involving a search for truth only about the empirical world, about what is actual and observable.... It must involve throughout a resolute rejection of the demand for an explanation of the regularities in the observable course of nature, by means of truths concerning a reality beyond what is actual and observable, as a demand which plays no role in the scientific enterprise.”
—Bas Van Fraassen (b. 1941)
“Nature, we are starting to realize, is every bit as important as nurture. Genetic influences, brain chemistry, and neurological development contribute strongly to who we are as children and what we become as adults. For example, tendencies to excessive worrying or timidity, leadership qualities, risk taking, obedience to authority, all appear to have a constitutional aspect.”
—Stanley Turecki (20th century)