Query Learning Strategies Using Boosting and Bagging

We present an algorithm for multiclass semi-supervised learning, which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semi-supervised learning algorithms use approaches such as one-versus-all to convert the ...

This paper proposes two new graph-based query strategies for active learning in a framework that is convenient to combine with semi-supervised learning based on label propagation. The first strategy selects instances independently to maximize the change ...

Deep learning perception models require a massive amount of labeled training data to achieve good performance. While unlabeled data is easy to acquire, the cost of labeling is prohibitive and could create a tremendous burden on companies or individuals. ...