Online learning represents an important family of machine learning
algorithms, in which a learner attempts to resolve an online prediction (or any
type of decision-making) task by learning a model/hypothesis from a sequence of
data instances one at a time. The goal of online learning is to ensure that the
online learner would make a sequence of accurate predictions (or correct
decisions) given the knowledge of correct answers to previous prediction or
learning tasks and possibly additional information. This is in contrast to many
traditional batch learning or offline machine learning algorithms that are
often designed to train a model in batch from a given collection of training
data instances. This survey aims to provide a comprehensive survey of the
online machine learning literatures through a systematic review of basic ideas
and key principles and a proper categorization of different algorithms and
techniques. Generally speaking, according to the learning type and the forms of
feedback information, the existing online learning works can be classified into
three major categories: (i) supervised online learning where full feedback
information is always available, (ii) online learning with limited feedback,
and (iii) unsupervised online learning where there is no feedback available.
Due to space limitation, the survey will be mainly focused on the first
category, but also briefly cover some basics of the other two categories.
Finally, we also discuss some open issues and attempt to shed light on
potential future research directions in this field.