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 …

Land-cover mapping in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

Various strategies for active learning have been proposed in the machine learning literature.
In uncertainty sampling, which is among the most popular approaches, the active learner …

Training on web-scale data can take months. But much computation and time is wasted on
redundant and noisy points that are already learnt or not learnable. To accelerate training …

Detecting incidental scene text is a challenging task because of multi-orientation,
perspective distortion, and variation of text size, color and scale. Retrospective research has …

As a way to relieve the tedious work of manual annotation, active learning plays important
roles in many applications of visual concept recognition. In typical active learning scenarios …

One of the principal bottlenecks in applying learning techniques to classification problems is
the large amount of labeled training data required. Especially for images and video …

Active learning aims to train an accurate prediction model with minimum cost by labeling
most informative instances. In this paper, we survey existing works on active learning from …

Background Supervised learning methods need annotated data in order to generate efficient
models. Annotated data, however, is a relatively scarce resource and can be expensive to …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …