g-computation - G-computation estimates outcomes under specified interventions by applying the g-formula to observed-data relationships.
G-prior - A g-prior, commonly called Zellner's g-prior, is a Bayesian prior for regression coefficients in a normal linear model whose covariance is proportional to...
G-set - The G-set is the general boundary of a version space in concept learning.
Gabbay's separation theorem - Gabbay's separation theorem is a result in temporal logic concerning the separation of past, present and future reasoning.
Gabor filter - A Gabor filter is a filter formed by combining a sinusoidal pattern with a Gaussian envelope.
Gabor transform - The Gabor transform is a time-frequency representation obtained by applying a Fourier transform to a signal viewed through a shifted Gaussian window.
Gain parameter - A gain parameter is a coefficient that scales a signal or a component of a system's response.
Gain ratio - Gain ratio is a decision-tree split criterion that divides information gain by the split information of the candidate test.
Gale - Church alignment algorithm - The Gale - Church alignment algorithm identifies corresponding sentences or small sentence groups in two versions of a text, typically written in different...
Galerkin approximation - A Galerkin approximation represents an unknown solution using a finite set of basis functions and chooses their coefficients so that the equation's residual...
Game theory - Game theory studies situations in which the outcome for each decision-maker depends on choices made by others as well as their own.
Game tree - A game tree represents the possible sequences of decisions in a game.
Gamma correction - Gamma correction is a nonlinear adjustment of image values used to account for, encode or modify the relationship between numerical values and light or...
Gamma distribution - The gamma distribution is a family of continuous probability distributions for positive quantities, commonly parameterized by a positive shape and a...
Gamma encoding - Gamma encoding is the use of a nonlinear transfer curve to represent linear-light image values as stored or transmitted code values.
Gamma function ( G ) - The gamma function, conventionally written with the uppercase Greek letter Gamma, extends the factorial relationship beyond nonnegative integer arguments.
Gated recurrent unit - A gated recurrent unit, usually shortened to GRU, is a neural-network component for processing ordered sequences.
Gather/scatter (vector addressing) - In vector addressing, gather and scatter are operations that read or write multiple data elements using a set of memory indices.
Gating mechanism - A gating mechanism is a component that regulates how information contributes to a neural-network calculation, commonly by multiplying a signal by an...
Gauss - Newton algorithm - The Gauss - Newton algorithm is an iterative method for nonlinear least squares.
Gaussian blur - Gaussian blur smooths an image by replacing each pixel with a weighted combination of neighboring values, using weights shaped like a Gaussian distribution.
Gaussian discriminant analysis - Gaussian discriminant analysis (GDA) is a classification approach that models the feature distribution within each class as Gaussian and combines those...
Gaussian error model - A Gaussian error model assumes that the difference between an observed response and its modeled mean follows a normal distribution under specified conditions.
Gaussian-Gaussian model - A Gaussian-Gaussian model combines a Gaussian observation model with a Gaussian prior for an unknown mean or related continuous quantity.
Gaussian mixture model - A Gaussian mixture model, or GMM, represents a probability distribution as a weighted sum of Gaussian component distributions.
Gaussian Naive Bayes - Gaussian Naive Bayes is a classification method that combines Bayes' rule with a Gaussian model for each numerical feature within each class and a...
Gaussian process - A Gaussian process is a collection of random variables for which every finite collection has a joint Gaussian distribution.
Gaussian process approximations - Gaussian process approximations are methods that make Gaussian process modeling computationally feasible when direct calculations are too expensive or...
Gaussian Process Classification - Gaussian process classification is a method that predicts category probabilities while also saying how uncertain it is, by placing a flexible, smooth prior...
Gaussian process emulator - A Gaussian process emulator is a statistical surrogate that learns the input-output behavior of a simulator using a Gaussian process model.
Gaussian Process Regression - Gaussian process regression is a probabilistic regression method that places a Gaussian process model over an unknown function and updates that model using...
Gaussian random variable - A Gaussian random variable is a random variable whose probability distribution is Gaussian, also called normal.
Gaussian scale mixture - A Gaussian scale mixture is a probability distribution formed by combining Gaussian distributions that share a location but vary in scale.
Gazetteer - A gazetteer in natural-language processing is a collection of names or terms used to help recognize matching expressions in text.
Gelbach decomposition - Gelbach decomposition is a regression method for dividing the change in a selected coefficient, between a base model and a fuller model, into additive...
GELU Function - The GELU function, short for Gaussian error linear unit, is a smooth neural-network activation defined by GELU(x) = x times Phi(x), where Phi is the...
GenDICE - GenDICE, short for Generalized stationary Distribution Correction Estimation, is a method for estimating distribution corrections from offline transition data.
Gene expression programming - Gene expression programming, or GEP, is an evolutionary method that searches for programs or expressions.
General data protection regulation - The General Data Protection Regulation, or GDPR, is the European Union's framework governing the processing of personal data and protecting individuals'...
General linear model - A general linear model represents numerical outcomes as linear combinations of unknown coefficients and specified predictors, plus errors.
General regression neural network - A general regression neural network, or GRNN, is a memory-based method for predicting numerical outcomes by combining the outcomes of stored training...
Generalization - Generalization is a model's ability to perform well on new cases it has never seen, rather than merely repeating answers about the examples it was trained on.
Generalization curve - A generalization curve is a plot of a model's performance, usually the loss, on training data and on unseen data side by side as training progresses...
Generalization gap - The generalization gap is the difference between a model's performance on its training data and its performance beyond those data.
Generalization hierarchy - A generalization hierarchy organizes concepts or descriptions from more specific to more general.
Generalization loss - Generalization loss is a model's expected loss on examples drawn from the target data distribution.