Generalized iterative scaling
In statistics, generalized iterative scaling (GIS) and improved iterative scaling (IIS) are two early algorithms used to fit log-linear models, notably multinomial logistic regression (MaxEnt) classifiers and extensions of it such as MaxEnt Markov models and conditional random fields. These algorithms have been largely surpassed by gradient-based methods such as L-BFGS and coordinate descent algorithms.
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- McCallum, Andrew; Freitag, Dayne; Pereira, Fernando (2000). "Maximum Entropy Markov Models for Information Extraction and Segmentation" (PDF). Proc. ICML 2000. pp. 591–598.
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- 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 (1–2): 41–75. doi:10.1007/s10994-010-5221-8.
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