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...

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

\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...

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...

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

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

{\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...

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...

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...

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...

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

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...

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

{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...

The most common loss function for regression is the square loss function (also known as the L2-norm). This familiar loss function is used in Ordinary...

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

minimization of a convex loss function over a convex set of functions. Specifically, the loss being minimized by AdaBoost is the exponential loss ∑ i ϕ ( i , y ...

a predefined loss function on given test data. The objective function takes a tuple of hyperparameters and returns the associated loss. Typically these...

[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...

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...

goal is to minimize a loss function L ( h ; x u , x v , y u , v ) {\displaystyle L(h;x_{u},x_{v},y_{u,v})} . The loss function typically reflects the...