Human intelligence comes from the capability to describe and make sense of the world
surrounding us, often in a lifelong manner. Online Learning (OL) allows a model to simulate …

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 …

Learning with streaming data has received extensive attention during the past few years.
Existing approaches assume the feature space is fixed or changes by following explicit …

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 …

This paper explores a new online learning problem where the data streams are generated
from an over-time varying feature space, in which the random variables are of mixed data …

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 …

Mining feature evolvable streams has gained increasing attention in recent years. However,
most existing approaches are designed for stationary data streams (ie, data streams without …

Trapezoidal Data Streams are an emerging topic, where not only the data volume increases,
but also the data dimension, ie new features emerge. In this paper, we address the …

This thesis extends the field of online learning by proposing algorithms that learn when
feature spaces can change as the input data stream in. The first three components of the …