Generalized iterative scaling

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.^[4]

References

^ Darroch, J.N. and Ratcliff, D. (1972). "Generalized iterative scaling for log-linear models". The Annals of Mathematical Statistics. 43 (5). Institute of Mathematical Statistics: 1470–1480.{{cite journal}}: CS1 maint: multiple names: authors list (link)
^ McCallum, Andrew; Freitag, Dayne; Pereira, Fernando (2000). "Maximum Entropy Markov Models for Information Extraction and Segmentation" (PDF). Proc. ICML 2000. pp. 591–598. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)
^ Malouf (2002). A comparison of algorithms for maximum entropy parameter estimation (PDF). Sixth Conf. on Natural Language Learning (CoNLL). pp. 49–55. {{cite conference}}: |first= missing |last= (help)
^ Yu, Hsiang-Fu; Huang, Fang-Lan; Lin, Chih-Jen (2011). "Dual coordinate descent methods for logistic regression and maximum entropy models" (PDF). Machine Learning. 85: 41–75.