optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of...

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...

learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy...

rule to neural networks. Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output...

Triplet loss is a machine learning loss function widely used in one-shot learning, a setting where models are trained to generalize effectively from limited...

In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most...

{\displaystyle g(z)} the logistic function as before. The logistic loss is sometimes called cross-entropy loss. It is also known as log loss.[duplication?] (In this...

The Taguchi loss function is graphical depiction of loss developed by the Japanese business statistician Genichi Taguchi to describe a phenomenon affecting...

\tau \right\}} where 0 < τ < 1 {\displaystyle 0<\tau <1} . Define the loss function as ρ τ ( u ) = u ( τ − I ( u < 0 ) ) = { ( τ − 1 ) u , if  u < 0 , τ...

regularization. This includes, for example, early stopping, using a robust loss function, and discarding outliers. Implicit regularization is essentially ubiquitous...

fiber Dielectric loss, a dielectric material's inherent dissipation of electromagnetic energy Loss function, in statistics, a function representing the...

mutations, are a form of loss-of-function mutations that completely prohibit the gene's function. The mutation leads to a complete loss of operation at the...

other methods by allowing optimization of an arbitrary differentiable loss function. The idea of gradient boosting originated in the observation by Leo...

central tendency; because a biased estimator gives a lower value of some loss function (particularly mean squared error) compared with unbiased estimators...

{\displaystyle f(t,x)} can be then learned by minimizing the following loss function L t o t {\displaystyle L_{tot}} : L t o t = L u + L f {\displaystyle...

value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior expectation of a utility function. An alternative...

values and the true value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly...

. Here, the value function v {\displaystyle v} is a non-linear (typically concave) function that mimics human loss aversion and risk aversion...

minimizes a predefined loss function on a given data set. The objective function takes a set of hyperparameters and returns the associated loss. Cross-validation...

{E} [L(h(x),y)]=\int L(h(x),y)\,dP(x,y).} A loss function commonly used in theory is the 0-1 loss function: L ( y ^ , y ) = { 1  if  y ^ ≠ y 0  if  y ^...

_{i}w(x)_{i}f_{i}(x)} . Both the experts and the weighting function are trained by minimizing some loss function, generally via gradient descent. There is much freedom...

squared error loss, is taken as a measure of the goodness of fit, and the best fit is obtained when that function is minimized. The log loss for the k-th...

metrics for probabilistic predictions or forecasts. While "regular" loss functions (such as mean squared error) assign a goodness-of-fit score to a predicted...

in function space unlike gradient boosting that works as gradient descent in function space, a second order Taylor approximation is used in the loss function...

comparisons of treatment means. However, loss functions were avoided by Ronald A. Fisher[clarification needed - loss functions weren't explicitly mentioned yet]...

example, with other convex loss functions. Consider the setting of supervised learning with f {\displaystyle f} being a linear function to be learned: f ( x...

(x_{i},\;y_{i})} . In order to measure how well a function fits the training data, a loss function L : Y × Y → R ≥ 0 {\displaystyle L:Y\times Y\to \mathbb...

[non-primary source needed] A modified version of SDF was introduced as a loss function to minimise the error in interpenetration of pixels while rendering...

between the hinge loss and these other loss functions is best stated in terms of target functions - the function that minimizes expected risk for a given...

linear-error loss respectively—which are more representative of typical loss functions—and for a continuous posterior distribution there is no loss function which...