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Download Machine Learning with Noisy Labels PDF – Definitions, Theory, Techniques & Solutions

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Download Machine Learning with Noisy Labels PDF – Definitions, Theory, Techniques & Solutions by Gustavo Carneiro. Explore label‑noise types, theoretical foundations, and state‑of‑the‑art methods for training robust ML models on imperfect datasets. Instant access to practical algorithms and best practices—boost your model accuracy today!

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Machine Learning With Noisy Labels Definitions
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Machine Learning with Noisy Labels Definitions
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Machine Learning with Noisy Labels Definitions

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Uploaded on
July 15, 2025
Number of pages
302
Written in
2024/2025
Type
Other
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  • noisy labels deep lea

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Machine Learning
With Noisy Labels

,
, Machine Learning
With Noisy Labels
Definitions, Theory, Techniques,
and Solutions

Gustavo Carneiro
Centre for Vision, Speech and Signal Processing (CVSSP)
Surrey Institute for People-centred Artificial Intelligence
Department of Electrical and Electronic Engineering
The University of Surrey
Guildford, United Kingdom

, Academic Press is an imprint of Elsevier
125 London Wall, London EC2Y 5AS, United Kingdom
525 B Street, Suite 1650, San Diego, CA 92101, United States
50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States
Copyright © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI
training, and similar technologies.
Publisher’s note: Elsevier takes a neutral position with respect to territorial disputes or jurisdictional
claims in its published content, including in maps and institutional affiliations.
No part of this publication may be reproduced or transmitted in any form or by any means, electronic
or mechanical, including photocopying, recording, or any information storage and retrieval system,
without permission in writing from the publisher. Details on how to seek permission, further
information about the Publisher’s permissions policies and our arrangements with organizations such
as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website:
www.elsevier.com/permissions.
This book and the individual contributions contained in it are protected under copyright by the
Publisher (other than as may be noted herein).
Notices
Knowledge and best practice in this field are constantly changing. As new research and experience
broaden our understanding, changes in research methods, professional practices, or medical treatment
may become necessary.
Practitioners and researchers must always rely on their own experience and knowledge in evaluating
and using any information, methods, compounds, or experiments described herein. In using such
information or methods they should be mindful of their own safety and the safety of others, including
parties for whom they have a professional responsibility.
To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume
any liability for any injury and/or damage to persons or property as a matter of products liability,
negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas
contained in the material herein.

ISBN: 978-0-443-15441-6


For information on all Academic Press publications
visit our website at https://www.elsevier.com/books-and-journals


Publisher: Mara Conner
Acquisitions Editor: Tim Pitts
Editorial Project Manager: Emily Thomson
Production Project Manager: Gomathi Sugumar
Cover Designer: Christian Bilbow
Typeset by VTeX

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