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Use coupon code “MARCH20” for a 20% discount on all items! Valid until 31-03-2025
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| Product ID: | 9780443154416 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | GB |
| Title: | Machine Learning with Noisy Labels |
| Subtitle: | Definitions, Theory, Techniques and Solutions |
| Authors: | Author: Gustavo , Surrey Institute for People-centred Artificial Intelligence, Department of Electrical and |
| Page Count: | 312 |
| Subjects: | Machine learning, Machine learning, Pattern recognition, Computer vision, Pattern recognition, Computer vision |
| Description: | Select Guide Rating 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. 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. |
| Imprint Name: | Academic Press Inc |
| Publisher Name: | Elsevier Science Publishing Co Inc |
| Country of Publication: | GB |
| Publishing Date: | 2024-03-18 |
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