Sentence examples for minimizing the classification from inspiring English sources

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.

The four classifiers are weighted as a function of minimizing the classification error using the limited labeled target data.

Each binary classifier is assigned a weight term where the weight terms are learned by combining the weighted classifier outputs, while minimizing the classification error of each domain.

Building a robust classifier when learning from a highly unbalanced dataset is very difficult; minimizing the classification error typically causes the larger class to overwhelm the smaller one.

Presently, operators of conventional sizers and colour sorters adjust the class boundaries manually based on observations of obvious misclassification trends in the packed fruit, with the goal of minimizing the classification errors.

The best number of features and parameters (C and γ) were obtained by minimizing the classification error of SVM based on 10 iterations of 10-fold cross-validation.

The mother wavelet was optimized to minimize the classification error estimated from the training set.

This formula represents an optimal classification hyperplane, which can minimize the classification error rate and maximize the classification interval in the meantime.

The chapter approaches the classification problem via Bayesian probabilistic arguments with a goal to minimize the classification error probability or the risk.

A weighted linear combination rule, called adapted minimum classification error (AMCE), is developed to concurrently minimize the classification errors and the log likelihood errors.

Generally, a cost-based feature selection method is used to maximize the classification performance and minimize the classification cost associated with the features, which is a multi-objective optimization problem.