ノイズの多いラベルを用いた機械学習：定義、理論、技術と解決策 Machine Learning with Noisy Labels : Definitions, Theory, Techniques and Solutions

Carneiro, Gustavo

Academic Press Inc（2024/03発売）

Academic Press Inc（2024/03発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 312 p.
• 言語 ENG
• 商品コード 9780443154416
• DDC分類 006.31

Full Description

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.

Contents

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