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Machine Learning with Noisy Labels - Definitions, Theory, Techniques and Solutions
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Machine Learning with Noisy Labels - Definitions, Theory, Techniques and Solutions
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Most of the modern machine learning models, based on deep learning
techniques, depend on carefully curated and cleanly labelled
training sets to be reliably trained and deployed. However, the
expensive labelling process involved in the acquisition of such
training sets limits the number and size of datasets available to
build new models, slowing down progress in the field.
Alternatively, many poorly curated training sets containing noisy
labels are readily available to be used to build new models.
However, the successful exploration of such noisy-label training
sets depends on the development of algorithms and models that are
robust to these noisy labels. Machine learning and Noisy Labels:
Definitions, Theory, Techniques and Solutions defines different
types of label noise, introduces the theory behind the problem,
presents the main techniques that enable the effective use of
noisy-label training sets, and explains the most accurate methods
developed in the field. This book is an ideal introduction to
machine learning with noisy labels suitable for senior
undergraduates, post graduate students, researchers and
practitioners using, and researching into, machine learning
methods.
General
Imprint: |
Academic Press Inc
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Country of origin: |
United States |
Release date: |
2024 |
First published: |
2024 |
Authors: |
Gustavo Carneiro
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Dimensions: |
234 x 156mm (L x W) |
Pages: |
200 |
ISBN-13: |
978-0-443-15441-6 |
Categories: |
Books
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LSN: |
0-443-15441-4 |
Barcode: |
9780443154416 |
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