With crowdsourcing systems, labels can be obtained with low cost, which facilitates the
creation of training sets for prediction model learning. However, the labels obtained from …

Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity,
and uncertainty, which lead the knowledge learning from them full of challenges. With the …

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 …

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 …

With the rapid growing of crowdsourcing systems, quite a few applications based on a
supervised learning paradigm can easily obtain massive labeled data at a relatively low …

Activity recognition in smart environment has been investigated rigorously in recent years.
Researchers are enhancing the underlying activity discovery and recognition process by …

An active learner is given a hypothesis class, a large set of unlabeled examples and the
ability to interactively query labels to an oracle of a subset of these examples; the goal of the …

Training data creation is increasingly a key bottleneck for developing machine learning,
especially for deep learning systems. Active learning provides a cost-effective means for …

Pool-based active learning (AL) aims to optimize the annotation process (ie, labeling) as the
acquisition of annotations is often time-consuming and therefore expensive. For this …

This paper studies the active learning problem in crowdsourcing settings, where multiple
imperfect annotators with varying levels of expertise are available for labeling the data in a …