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)
“If you love the good thing vitally, enough to give up for it all that one must give up, then you must hate the cheap thing just as hard. I tell you, there is such a thing as creative hate! A contempt that drives you through fire, makes you risk everything and lose everything, makes you a long sight better than you ever knew you could be.”
—Willa Cather (1873–1947)