1 Introduction
This report provides a general review of the literature on active learning. There
have been a host of algorithms and applications for learning with queries over
the years, and this document is an attempt to distill the core ideas, methods, and
applications that have been considered by the machine learning community. To
make this survey more useful in the long term, an online version will be updated
and maintained indefinitely at:
http://active-learning.net/
When referring to this document, I recommend using the following citation:
Burr Settles. Active Learning Literature Survey. Computer Sciences Tech-
nical Report 1648, University of Wisconsin–Madison. 2009.
An appropriate BIBT
E
X entry is:
@techreport{settles.tr09,
Author = {Burr Settles},
Institution = {University of Wisconsin--Madison},
Number = {1648},
Title = {Active Learning Literature Survey},
Type = {Computer Sciences Technical Report},
Year = {2009},
}
This document is written for a machine learning audience, and assumes the reader
has a working knowledge of supervised learning algorithms (particularly statisti-
cal methods). For a good introduction to general machine learning, I recommend
Mitchell (1997) or Duda et al. (2001). I have strived to make this review as com-
prehensive as possible, but it is by no means complete. My own research deals pri-
marily with applications in natural language processing and bioinformatics, thus
much of the empirical active learning work I am familiar with is in these areas.
Active learning (like so many subfields in computer science) is rapidly growing
and evolving in a myriad of directions, so it is difficult for one person to provide
an exhaustive summary. I apologize for any oversights or inaccuracies, and en-
courage interested readers to submit additions, comments, and corrections to me
at: bsettles@cs.cmu.edu.
3
