Supervised learning techniques construct predictive models by learning from a large
number of training examples, where each training example has a label indicating its ground …

Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large
groups of individuals toward solving problems. Common problems approached with …

The rapid growth and adaptation of medical information to identify significant health trends
and help with timely preventive care have been recent hallmarks of the modern healthcare …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

Any important data management and analytics tasks cannot be completely addressed by
automated processes. These tasks, such as entity resolution, sentiment analysis, and image …

Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

Data are often labeled by many different experts with each expert only labeling a small
fraction of the data and each data point being labeled by several experts. This reduces the …

Inferring rankings over elements of a set of objects, such as documents or images, is a key
learning problem for such important applications as Web search and recommender systems …

In all these cases, labels can be obtained, but only at a significant cost to the end user. An
important observation is that all records are not equally important from the perspective of …