Generalized iterative scaling

In statistics, generalized iterative scaling (GIS) and improved iterative scaling (IIS) are two early algorithms used to fit log-linear models,{{Cite journal |title=Generalized iterative scaling for log-linear models |author1=Darroch, J.N. |author2=Ratcliff, D. |journal=The Annals of Mathematical Statistics |volume=43 |issue=5 |pages=1470–1480 |year=1972 |url=http://projecteuclid.org/download/pdf_1/euclid.aoms/1177692379 |doi=10.1214/aoms/1177692379|doi-access=free }} notably multinomial logistic regression (MaxEnt) classifiers and extensions of it such as MaxEnt Markov models{{Cite conference|last = McCallum|first = Andrew|last2 = Freitag|first2 = Dayne|last3 = Pereira|first3 = Fernando|title = Maximum Entropy Markov Models for Information Extraction and Segmentation|book-title = Proc. ICML 2000|year = 2000|pages = 591–598|url=http://www.ai.mit.edu/courses/6.891-nlp/READINGS/maxent.pdf}} and conditional random fields. These algorithms have been largely surpassed by gradient-based methods such as L-BFGS{{cite conference |first=Robert |last=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 }} and coordinate descent algorithms.{{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 }}