An Online Semi-supervised Active Learning Algorithm with Self-organiing Incremental Neural Network

Shen Furao, Keisuke Sakurai, Youki Kamiya, Osamu Hasegawa

Abstract: An online semi-supervised active learning algorithm is proposed, which is based on self-organizing incremental neural network (SOINN). The proposed method do not need any priori knowledge such as number of nodes or number of classes; it can automatically learn number of nodes and teacher vectors needed by the current task; It can realize online incremental learning even life-long learning. The experiments for artificial data and real world data show that the proposed method works efficiently.