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Various metrics that define the interestingness of a pattern (support, lift, maximal entropy, etc) for subsequent ranking and filtering of retrieved patterns have been studied at theoretical and experimental level.
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In the maximal entropy principle, assuming stationarity, one looks for the probability distribution which maximizes the statistical entropy given those constraints.
Those conclusions where obtained using the maximal entropy principle[14].
We give an answer to this question using a maximal entropy approach.
In statistical physics, they are usually derived from the maximal entropy principle [14].
Several typical choices for the training criterion (e.g., squared loss, cross entropy, etc).
The more commonly used method of logistic regression is compared with a newer method of maximal entropy (MaxEnt).
The infinite range of the potential corresponds, in the maximal entropy principle interpretation, to having infinitely many constraints.
In this way, the truncated potential corresponds to a finite number of constraints in the maximal entropy principle interpretation.
The most known ones for opinion mining are: Naïve Bayesian classification, maximal entropy principle, and the support vector machine.
A maximal entropy production principle (MEPP) for chemical reactions was proposed to develop a sharp interface model for rapid solidification.
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