In machine learning, active learning refers to algorithms that autonomously select the data
points from which they will learn. There are many data mining applications in which large …

In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …

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 …

Active learning strives to reduce annotation costs by choosing the most critical examples to
label. Typically, the active learning strategy is contingent on the classification model. For …

We study the problem of selecting a subset of big data to train a classifier while incurring
minimal performance loss. We show the connection of submodularity to the data likelihood …

A typical camera trap survey may produce millions of images that require slow, expensive
manual review. Consequently, critical conservation questions may be answered too slowly …

Most active learning approaches select either informative or representative unlabeled
instances to query their labels. Although several active learning algorithms have been …

Text mining is a new and exciting area of computer science research that tries to solve the
crisis of information overload by combining techniques from data mining, machine learning …

Natural language processing (NLP) and neural networks (NNs) have both undergone
significant changes in recent years. For active learning (AL) purposes, NNs are, however …

We propose an active learning approach for transferring representations across domains.
Our approach, active adversarial domain adaptation (AADA), explores a duality between two …