Hence, instead of training just one SVM classifier, we aim to reduce classification errors by training an ensemble of five classifiers, each of which uses all 100 manually labeled reviews and a different subset of the 500 automatically labeled reviews.
we partition the 500 automatically labeled reviews into five equal-sized sets as follows.