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This article extends the standard effort-accuracy approach to explaining task influences on decision processing by arguing that coping goals will interact with effort minimization and accuracy maximization goals for negatively emotion-laden decision tasks.
Accuracy maximization, complexity minimization and interpretability maximization have often been used for multi-objective design of fuzzy rule-based classifiers.
The proposed framework simultaneously considers the accuracy maximization and the complexity minimization objectives.
However, end-users process information dynamically across different judgmental contexts and sometimes favor effort reduction over accuracy maximization.
We use training based on Expectation Maximization for this fully unsupervised technique.
Multiple expectation maximization for motif elicitation.
Indeed, [16, 17] provide upper bound maximization for the original nonconvex maximization, where lower bound maximization should always be preferred.
using dedicated ordered subsets expectation maximization for multiplexing multipinhole reconstruction.
We find statistically and economically significant departures from expected utility maximization for many subjects.
Step 2: Expectation maximization for model fitting.
There was no problem of maximization for the naive approach.
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