In [[statistics]], '''GISgeneralized iterative scaling''' ('''generalized iterative scaling)GIS''') and '''improved iterative scaling''' ('''IIS''') are two early [[algorithm]]s used to fit [[log-linear model]]s,<ref>{{Cite journal |title=Generalized iterative scaling for log-linear models |authorauthor1=Darroch, J.N. and |author2=Ratcliff, D. |journal=The Annals of Mathematical Statistics |volume=43 |issue=5 |pages=1470–1480 |year=1972 |publisher=Institute of Mathematical Statistics |url=http://projecteuclid.org/download/pdf_1/euclid.aoms/1177692379 |doi=10.1214/aoms/1177692379|doi-access=free }}</ref> notably [[multinomial logistic regression]] (MaxEnt) [[Statistical classification|classifiers]] and extensions of it such as [[Maximum-entropy Markov model|MaxEnt Markov models]]<ref>{{Cite conference|last = McCallum|first = Andrew|last2 = Freitag|first2 = Dayne|last3 = Pereira|first3 = Fernando|title = Maximum Entropy Markov Models for Information Extraction and Segmentation|booktitlebook-title = Proc. ICML 2000|year = 2000|pages = 591–598|url=http://www.ai.mit.edu/courses/6.891-nlp/READINGS/maxent.pdf}}</ref> and [[conditional random field]]s. These algorithms have been largely surpassed by gradient-based methods such as [[L-BFGS]]<ref name="malouf">{{cite conference |first=Robert |last2last=Malouf |year=2002 |url=http://acl.ldc.upenn.edu/W/W02/W02-2018.pdf |title=A comparison of algorithms for maximum entropy parameter estimation |conference=Sixth Conf. on Natural Language Learning (CoNLL) |pages=49–55 |url-status=dead |archive-url=https://web.archive.org/web/20131101205929/http://acl.ldc.upenn.edu/W/W02/W02-2018.pdf |archive-date=2013-11-01 }}</ref> and [[coordinate descent]] algorithms.<ref>{{cite journal |first1=Hsiang-Fu |last1=Yu |first2=Fang-Lan |last2=Huang |first3=Chih-Jen |last3=Lin |year=2011 |title=Dual coordinate descent methods for logistic regression and maximum entropy models |journal=Machine Learning |volume=85 |issue=1–2 |pages=41–75 |url=http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf |doi=10.1007/s10994-010-5221-8|doi-access=free }}</ref>
