2 research outputs found
Learning from partial correction
We introduce a new model of interactive learning in which an expert examines
the predictions of a learner and partially fixes them if they are wrong.
Although this kind of feedback is not i.i.d., we show statistical
generalization bounds on the quality of the learned model.Comment: 13 pages, 2 figure
Structural query-by-committee
In this work, we describe a framework that unifies many different interactive
learning tasks. We present a generalization of the {\it query-by-committee}
active learning algorithm for this setting, and we study its consistency and
rate of convergence, both theoretically and empirically, with and without
noise