Evaluation parameters of ensemble classifiers with overlapped partitioning on data set A.

The data set A was evaluated by cross-validation (900 training data ) and cross-validation (8100 training data). The number of weak learners and the number of blocks were the parameters of the overlapped partitioning. These evaluation methods and parameters determine the amount of training data for a weak learner in an ensemble classifier. The number of training letters (#training letters) is decided by 50 entire letters × . The number of training data for a weak learner (#training data for a weak learner) can be computed by 9000 entire ERPs × × /.