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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Theory of Disagreement-Based Active Learning (Paperback)
Loot Price: R2,362
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Theory of Disagreement-Based Active Learning (Paperback)
Series: Foundations and Trends (R) in Machine Learning
Expected to ship within 10 - 15 working days
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Active learning is a protocol for supervised machine learning in
which a learning algorithm sequentially requests the labels of
selected data points from a large pool of unlabeled data. This
contrasts with passive learning where the labeled data are taken at
random. The objective in active learning is to produce a
highly-accurate classifier, ideally using fewer labels than the
number of random labeled data sufficient for passive learning to
achieve the same. Theory of Disagreement-Based Active Learning
describes recent advances in our understanding of the theoretical
benefits of active learning, and implications for the design of
effective active learning algorithms. Much of the monograph focuses
on a particular technique, namely disagreement-based active
learning, which by now has amassed a mature and coherent
literature. It also briefly surveys several alternative approaches
from the literature. The emphasis is on theorems regarding the
performance of a few general algorithms, including rigorous proofs
where appropriate. However, the presentation is intended to be
pedagogical, focusing on results that illustrate fundamental ideas
rather than obtaining the strongest or most generally known
theorems. Theory of Disagreement-Based Active Learning is intended
for researchers and advanced graduate students in machine learning
and statistics who are interested in gaining a deeper understanding
of the recent and ongoing developments in the theory of active
learning.
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