Examples Required for Random Example Selection Learning

In many real-world domains like text categorization, supervised learning requires a large number of training examples. In our research, we are using active learning with committees methods to reduce the number of training examples required for learning. Disagreement among the committee members on the predicted label for the input part of each example is used to determine the need for knowing the actual value of the label. Qur experiments in text categorization using this approach demonstrate a l-2 orders of magnitude reduction in the number of labeled training examples required.