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Bayesian classifier minimizes classification error.
The classification error rate of this model was reported.
Cross-panel classification error rates among various CIMP classification panels, expressed as percentages.
Adaptive network structure is employed to minimize classification error.
This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN).
While discussing the concept of minimizing the classification error probability, it is shown that the Bayesian classifier is optimal with respect to minimizing the classification error probability.
A noise feature is one that, when added to the document representation, increases the classification error on new data.
For each step, we greedily train a newly-added convolutional layer in the segmentation branch to minimize the classification error.
In Section 13.1 (page ), we said that we want to minimize classification error on the test set.
For some problems, there exists a nonlinear classifier with zero classification error, but no such linear classifier.
In Section 13.1, we stated as our goal in text classification the minimization of classification error on test data.
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