Loss function In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number ...
 Risk function In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number ...
 Regret (decision theory) Regret is the negative emotion experienced when learning that an alternative course of action would have resulted in a more favorable outcome. The theory of regret aversion or anticipated regret propo...
 Mean squared prediction error In statistics the mean squared prediction error of a smoothing or curve fitting procedure is the expected value of the squared difference between the fitted values and the (unobservable) function g. ...
 Huber loss function In statistics, the Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. A variant for classification is also sometimes used....
 Sum of absolute transformed differences Sum of absolute transformed differences (SATD) is a widely used video quality metric used for block-matching in motion estimation for video compression. It works by taking a frequency transform, usua...
 Hinge loss In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs).For an...
 Mean squared error In statistics, the mean squared error (MSE) of an estimator measures the average of the squares of the "errors", that is, the difference between the estimator and what is estimated. MSE is a risk fu...
 Taguchi loss function The Taguchi Loss Function is graphical depiction of loss developed by the Japanese business statistician Genichi Taguchi to describe a phenomenon affecting the value of products produced by a company...
 Sum of absolute differences In digital image processing, the sum of absolute differences (SAD) is an algorithm for measuring the similarity between image blocks. It works by taking the absolute difference between each pixel in ...