Active Learning. Burr Settles
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Active Learning
Synthesis Lectures on Artificial Intelligence and Machine Learning
Editor
Ronald J. Brachman, Yahoo! Research
William W. Cohen, Carnegie Mellon University
Thomas Dietterich, Oregon State University
Active Learning
Burr Settles
2012
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Copyright © 2012 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.
Active Learning
Burr Settles
www.morganclaypool.com
ISBN: 9781608457250 paperback
ISBN: 9781608457267 ebook
DOI 10.2200/S00429ED1V01Y201207AIM018
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Lecture #18
Series Editors: Ronald J. Brachman, Yahoo Research
William W. Cohen, Carnegie Mellon University
Thomas Dietterich, Oregon State University
Series ISSN
Synthesis Lectures on Artificial Intelligence and Machine Learning
Print 1939-4608 Electronic 1939-4616
Active Learning
Burr Settles
Carnegie Mellon University
SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING #18
ABSTRACT
The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose “queries,” usually in the form of unlabeled data instances to be labeled by an “oracle” (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain.
This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or “query selection frameworks.” We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
KEYWORDS
active learning, expected error reduction, hierarchical sampling, optimal experimental design, query by committee, query by disagreement, query learning, uncertainty sampling, variance reduction
Dedicated to my family and friends, who keep me asking questions.
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