Maximum Entropy-based Learning Algorithm

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A Maximum Entropy-based Learning Algorithm is a supervised discriminative classification algorithm that favors class predictions with maximum entropy (least biased estimates).



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  • (Jaynes, 1957) ⇒ E. T. Jaynes. (1957). “Information Theory and Statistical Mechanics.
    • "Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximum entropy estimate. It is least biased estimate possible on the given information; i.e., it is maximally noncommittal with regard to missing information.