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 …

Online portfolio selection is a fundamental problem in computational finance, which has
been extensively studied across several research communities, including finance, statistics …

LIBOL is an open-source library for large-scale online learning, which consists of a large
family of efficient and scalable state-of-the-art online learning algorithms for large-scale …

We present AROW, a new online learning algorithm that combines several properties of
successful: large margin training, confidence weighting, and the capacity to handle non …

In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme,
which enables the conventional confidence-weighted learning method to handle non …

On-line portfolio selection, a fundamental problem in computational finance, has attracted
increasing interest from artificial intelligence and machine learning communities in recent …

Online portfolio selection has been attracting increasing attention from the data mining and
machine learning communities. All existing online portfolio selection strategies focus on the …

We present AROW, an online learning algorithm for binary and multiclass problems that
combines large margin training, confidence weighting, and the capacity to handle non …

The first step in graph-based semi-supervised classification is to construct a graph from input
data. While the k-nearest neighbor graphs have been the de facto standard method of graph …

In this paper, we study a new problem of continuous learning from doubly-streaming data
where both data volume and feature space increase over time. We refer to the doubly …