Title
Online learning: A comprehensive survey
Abstract
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 of data instances one by one at each time. The goal of online learning is to maximize the accuracy/correctness for the sequence of predictions/decisions made by the online learner given the knowledge of correct answers to previous prediction/learning tasks and possibly additional information. This is in contrast to traditional batch or offline machine learning methods that are often designed to learn a model from the entire training data set at once. Online learning has become a promising technique for learning from continuous streams of data in many real-world applications. This survey aims to provide a comprehensive survey of the online machine learning literature through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the types of learning tasks and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) online supervised learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) online unsupervised learning where no feedback is 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.
Year
DOI
Venue
2018
10.1016/j.neucom.2021.04.112
Neurocomputing
Keywords
Field
DocType
Online learning,Online convex optimization,Sequential decision making
Training set,Online learning,Online machine learning,Categorization,Artificial intelligence,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
459
0925-2312
10
PageRank 
References 
Authors
0.65
114
4
Search Limit
100114
Name
Order
Citations
PageRank
Steven C. H. Hoi126817.70
Doyen Sahoo2839.94
Jing Lu3554.74
Peilin Zhao4136580.09