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.

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