Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

Life-long learning aims at learning a sequence of tasks without forgetting the previously
acquired knowledge. However, the involved training data may not be life-long legitimate due …

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 …

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 …

In many dynamic environment applications, with the evolution of data collection ways, the
data attributes are incremental and the samples are stored with accumulated feature spaces …

With the emerging of new data collection ways, the features are incremental and
accumulated gradually. Due to the expansion of feature spaces, it is more common that …

This paper explores a new online learning problem where the input sequence lives in an
over-time varying feature space and the ground-truth label of any input point is given only …

In this paper, we propose a new online learning algorithm tailored for data streams
described by varying feature spaces (VFS), wherein new features constantly emerge and old …

Sparse online learning has received extensive attention during the past few years. Most of
existing algorithms that utilize ℓ1-norm regularization or ℓ1-ball projection assume that the …

The visual perception systems aim to autonomously collect consecutive visual data and
perceive the relevant information online like human beings. In comparison with the classical …