Active Learning of the Generalized High-Low-Game

Hasenjäger M, Ritter H. Active Learning of the Generalized High-Low-Game. In: Malsburg von der C, Seelen von W, Vorbrüggen J, Sendhoff B, eds. Artificial Neural Networks — ICANN 96: 1996 International Conference Bochum, Germany, July 16–19, 1996 Proceedings. Lecture Notes in Computer Science. Vol 1112. Berlin: Springer; 1996: 501-506.In this paper, we study the performance of active learning with the query algorithm Query by Committee (QBC), which selects a new query such that it approximately maximizes the expected information gain. As target functions, we introduce a generalization of the High-Low-Game, for which we derive a theoretically optimal query sequence. This allows us to compare the performance of a QBC-learner with an information-optimal active learner. Simulations show that an active learner that selects queries with QBC rapidly converges against a learner trained with theoretically optimal queries