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Bayesian classifier minimizes classification error.
The abstract representation allows for faster and safer comparisons of similarities between images because the discrete domain of features minimizes classification errors.
Especially in CBIR tasks, where the objective is not to find the one and only similar image but a set of k top correctly retrieved results, this discrete domain of features minimizes classification errors [43].
The optimal weights for this speaker identification scheme can be obtained by using the MCE-based discriminative training algorithm [5, 12, 14 16], which aims at deriving a set of speaker models which minimizes classification errors, that is, speaker identification errors for training data.
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Adaptive network structure is employed to minimize classification error.
In Section 13.1 (page ), we said that we want to minimize classification error on the test set.
The GA was used to optimize pattern recognition performance on training sets using a figure of merit designed to maximize correct classification of acceptable samples and minimize classification of unacceptable samples or samples of dissimilar materials.
In the classification task, whether it is to lessen the computation time, or to minimize classification errors, any method may be chosen based on its efficiency and ability to complete the task as required.
The optimum rank was selected to minimize classification error of the cross-validated samples and was determined to be 3 latent variables.
An optimal linear classifier then can be found by minimizing classification error (probability of misclassification).
The combination of the answers given to the respective question was necessary in order to minimize classification errors.
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