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In general, estimating conditional class probabilities for various classification techniques is termed probability estimation [57] or class probability estimation in the literature [58, 59].
During prediction, BA uses Bayesian posterior probability estimation.
Class probability estimation uses the information of the trained classifier.
For OVR-SVM, the most crucial part is probability estimation.
The Bayesian probability estimation was applied in this study to assess seismic hazard.
The second method, CS-EM, modifies EM by incorporating misclassification cost into the probability estimation process.
The use of a dedicated probability estimation table decreases the internal memory.
The probability estimation is best conducted through Monte Carlo simulations with variance reduction techniques.
Subsequently, the response analysis and failure probability estimation is carried out using Monte Carlo simulations.
The SVM classifier is configured with radial basis function (RBF) kernel and probability estimation [16].
Besides, a score calibration method and a probability estimation algorithm are detailed in this section.
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