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