However, the training time increases linearly by the number of boosting iteration.
to apply boosting to the semi-Markov perceptron, the following methods are proposed; 1) Use the weights of training samples decided by AdaBoost as the learning ratios of the semi-Markov perceptron, and 2) training on AdaBoost with the loss between the correct output of a training sample and the incorrect output that has the highest score.