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 …

This paper investigates a new online learning problem with doubly-streaming data, where
the data streams are described by feature spaces that constantly evolve, with new features …

Feature selection, an essential technique in data mining, is often confined to batch learning
or online idealization of data scenarios despite its significance. Existing online feature …

The idea of combining the active query strategy and the passive-aggressive (PA) update
strategy in online learning can be credited to the PA active (PAA) algorithm, which has …

In this paper, we explore a novel online learning setting, where the online learners are
presented with “doubly-streaming” data. Namely, the data instances constantly streaming in …

In this paper, we propose a new method for online learning in varying feature spaces (VFS)
where the feature space of instances continually evolves. The proposed method, termed …

Most conventional literature on online learning implicitly assumes a static feature space.
However, in real-world applications, the feature space may vary over time due to the …