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)
“I’m a very smart guy. I haven’t a feeling or a scruple in the world. All I have the itch for is money. I am so money greedy that for twenty-five bucks a day and expenses, mostly gasoline and whisky, I do my thinking myself, what there is of it; I risk my whole future, the hatred of the cops ... I dodge bullets and eat saps, and say thank you very much, if you have any more trouble, I hope you’ll think of me, I’ll just leave one of my cards in case anything comes up.”
—Raymond Chandler (1888–1959)