Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

In many real-world tasks, there are abundant unlabeled examples but the number of labeled
training examples is limited, because labeling the examples requires human efforts and …

Every day, huge volumes of sensory, transactional, and web data are continuously
generated as streams, which need to be analyzed online as they arrive. Streaming data can …

An increasing number of real-world applications are associated with streaming data drawn
from drifting and nonstationary distributions that change over time. These applications …

Online learning with feature spaces that are not fixed but can vary over time renders a
seemingly flexible learning paradigm thus has drawn much attention. Unfortunately, two …

Transfer learning has benefited many real‐world applications where labeled data are
abundant in source domains but scarce in the target domain. As there are usually multiple …

Semi-supervised clustering is a new learning method which combines semi-supervised
learning (SSL) and cluster analysis. It is widely valued and applied to machine learning …

In the world of modern technology, digital data are generated at a lightning speed. These
data are typically unlabeled as obtaining labels often requires time-consuming and costly …

We consider the problem of labeling points on a fast-moving data stream when only a small
number of labeled examples are available. In our setting, incoming points must be …

A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in
order to redetect objects over succeeding frames. Although these methods usually deliver …