Generalized iterative scaling

In statistics, generalized iterative scaling (GIS) and improved iterative scaling (IIS) are two early algorithms used to fit log-linear models,^[1] notably multinomial logistic regression (MaxEnt) classifiers and extensions of it such as MaxEnt Markov models^[2] and conditional random fields. These algorithms have been largely surpassed by gradient-based methods such as L-BFGS^[3] and coordinate descent algorithms.^[4]

See also

• Expectation-maximization