Generalized Iterative Scaling (GIS) is an algorithm used primarily in the context of statistical modeling and machine learning, particularly for optimizing the weights of a probabilistic model that adheres to a specified distribution. It is particularly useful for tasks involving maximum likelihood estimation (MLE) in exponential family distributions, which are common in various applications like natural language processing and classification tasks.
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