Machine Learning with Noisy Labels

Definitions, Theory, Techniques and Solutions

Gustavo Carneiro (Autor)

Buch | Softcover

312 Seiten

2024
Academic Press Inc (Verlag)
978-0-443-15441-6 (ISBN)

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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.

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

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

Erscheinungsdatum	12.03.2024
Verlagsort	San Diego
Sprache	englisch
Maße	156 x 234 mm
Gewicht	450 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
ISBN-10	0-443-15441-4 / 0443154414
ISBN-13	978-0-443-15441-6 / 9780443154416
Zustand	Neuware