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Active Learning

Synthesis Lectures on Artificial Intelligence and Machine Learning

Burr Settles​‌
Carnegie Mellon University

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.

Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations

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Abstract | PDF (7939 KB) | PDF Plus (8106 KB) 
Jorge Martínez-Palomera, Francisco Förster, Pavlos Protopapas, Juan Carlos Maureira, Paulina Lira, Guillermo Cabrera-Vives, Pablo Huijse, Lluis Galbany, Thomas de Jaeger, Santiago González-Gaitán, Gustavo Medina, Giuliano Pignata, Jaime San Martín, Mario Hamuy, Ricardo R. Muñoz​‌. (2018) The High Cadence Transit Survey (HiTS): Compilation and Characterization of Light-curve Catalogs. The Astronomical Journal 156:5, 186.
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Author:
Burr Settles
Keywords:
active learning
expected error reduction
hierarchical sampling
optimal experimental design
query by committee
query by disagreement
query learning
uncertainty sampling
variance reduction
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