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...

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

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...

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

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...

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

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

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...

Triplet loss is a loss function for machine learning algorithms where a reference input (called anchor) is compared to a matching input (called positive)...

{\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...

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

128-dimensional Euclidean space. The system uses the triplet loss function as its cost function and introduced a new online triplet mining method. The system...

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...

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

\right\}} where τ ∈ ( 0 , 1 ) . {\displaystyle \tau \in (0,1).} Define the loss function as ρ τ ( m ) = m ( τ − I ( m < 0 ) ) {\displaystyle \rho _{\tau }(m)=m(\tau...

function In economics, the cost curve, expressing production costs in terms of the amount produced. In mathematical optimization, the loss function,...

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

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...

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

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...

risk with respect to the absolute-deviation loss function, as observed by Laplace. Other loss functions are used in statistical theory, particularly...

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

that function; the procedure is then known as gradient ascent. It is particularly useful in machine learning for minimizing the cost or loss function. Gradient...

{\displaystyle x\mapsto {\hat {x}}:=(1,x)} , and consider the following loss function RSS x ⁡ ( A ) = ∑ i = 1 N ( y i − A x ^ i ) T w i ( x ) ( y i − A 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...

In mathematics, an indicator function or a characteristic function of a subset of a set is a function that maps elements of the subset to one, and all...

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

weight receives an update proportional to the partial derivative of the loss function with respect to the current weight. The problem is that as the network...

understanding and mitigation of systemic risk. Systemic risk Loss function Loss function § Expected loss Potential future exposure Sandra Thompson/Voon Hoe Chen...

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