1. A method for learning of a classifier using a plurality of samples, the method comprising: adding unlabeled samples to existing samples; estimating a maximum distance from any of samples to a center of all support vectors; estimating a minimum distance from any of samples to the center; setting a threshold between the maximum distance and the minimum distance to judge whether the newly added unlabeled sample are positive samples or not; determining whether the newly added unlabeled samples are candidate samples for new support vectors or not according to the threshold; extracting the candidate samples for new support vectors from the added unlabeled samples; and selecting the new support vectors from the candidate samples using a plurality of existing support vectors.