EAN: 9783031004322

SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE A...

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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