A Formulation for Active Learning with Applications to Object Detection
We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov''s optimal experiment design to the learning problem. We specifically show how to {\it analytically} derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.