Repairing Self-Confident Active-Transductive Learners Using Systematic Exploration
We consider an active learning game within a transductive learning model. A major problem with many active learning algorithms is that an unreliable current hypothesis can mislead the querying component to query ``uninformative'' points. In this work we propose a remedy to this problem. Our solution can be viewed as a ``patch'' for fixing this deficiency and also as a proposed modular approach for active-transductive learning that produces powerful new algorithms. Extensive experiments on ``real'' data demonstrate the advantage of our method.