Incremental active learning with bias reduction
Masashi Sugiyama, H. Ogawa · 2000
The problem of designing input signals for optimal generalization in supervised learning is called active learning. In many active learning methods devised so far, the bias of the learning results is assumed to be zero. In this paper, we remove this assumption and propose a new active learning method with the bias reduction. The effectiveness of the proposed method is demonstrated through computer simulations.