Active Learning

Active Learning

Burr Settles,
ISBN: 9781608457250 | PDF ISBN: 9781608457267
Copyright © 2012 | 114 Pages | Publication Date: 01/01/2012

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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.

Table of Contents

Automating Inquiry
Uncertainty Sampling
Searching Through the Hypothesis Space
Minimizing Expected Error and Variance
Exploiting Structure in Data
Practical Considerations

About the Author(s)

Burr Settles, Carnegie Mellon University
Burr Settles is a Postdoctoral Research Scientist in the Machine Learning Department at Carnegie Mellon University. He received a PhD in computer sciences from the University of Wisconsin-Madison in 2008, with additional studies in linguistics and biology. His research focuses on machine learning technology that interacts with humans as part of knowledge acquisition and training, with applications in natural language processing, biological discovery, and social computing. Burr is the author of more than 20 research papers, including a popular literature survey on active learning (which was the genesis for this book). He had organized workshops at the International Conference on Machine Learning (ICML) and the North American Chapter of the Association for Computational Linguistics (NAACL) on active learning and related topics in cost-sensitive machine learning. In his spare time he runs a website community for musicians, prefers sandals to shoes, and plays guitar in the Pittsburgh pop band Delicious Pastries.


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