Machine Learning with Noisy Labels

Definitions, Theory, Techniques and Solutions
Erscheinungsjahr 2024
ISBN: 978-0-443-15441-6
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 156 mm x 234 mm, Gewicht: 650 g

Machine Learning and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine learning with noisy labels that is suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching, machine learning methods. Most of the modern machine learning models based on deep learning techniques depend on carefully curated and cleanly labeled training sets to be reliably trained and deployed. However, the expensive labeling 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. This book defines the 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.

Autoren/Hrsg.

Carneiro, Gustavo

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

1. Problem Definition
2. Noisy-label Problems and Datasets
3. Theoretical Aspects of Noisy-label Learning
4. Noisy-Label Learning Techniques
5. Benchmarks, Methods, Results and Code
6. Conclusion and Final Considerations

Über Autor(innen)

Carneiro, Gustavo
Professor Gustavo Carneiro, Artificial Intelligence and Machine Learning, University of Surrey, UK.