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Generalized iterative scaling
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<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, generalized iterative scaling (GIS) and improved iterative scaling (IIS) are two early <a href="/facts/Algorithm/fnl5NmRt">algorithms</a> used to fit <a href="/facts/Log-linear_model/eCMocrl5">log-linear models</a>, notably <a href="/facts/Multinomial_logistic_regression/aeF5lqDE">multinomial logistic regression</a> (MaxEnt) <a href="/facts/Statistical_classification/jXXHRkXR">classifiers</a> and extensions of it such as <a href="/facts/Maximum-entropy_Markov_model/tkaRW6xy">MaxEnt Markov models</a> and <a href="/facts/Conditional_random_field/TNuLaGzM">conditional random fields</a>. These algorithms have been largely surpassed by gradient-based methods such as <a href="/facts/L-BFGS/spe3MAb3">L-BFGS</a> and <a href="/facts/Coordinate_descent/wnU1rHLp">coordinate descent</a> algorithms. </p>