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)
“Maybe we were the blind mechanics of disaster, but you don’t pin the guilt on the scientists that easily. You might as well pin it on M motherhood.... Every man who ever worked on this thing told you what would happen. The scientists signed petition after petition, but nobody listened. There was a choice. It was build the bombs and use them, or risk that the United States and the Soviet Union and the rest of us would find some way to go on living.”
—John Paxton (1911–1985)