Machine learning with noisy labels: definitions, theory, techniques, and solutions
Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling process involved in the acquisition of such training sets limits the number and size of...
Gespeichert in:
| Beteilige Person: | |
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
San Diego, CA
Academic Press
[2024]
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| Ausgabe: | First edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9780443154423/?ar |
| Zusammenfassung: | Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling 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. Alternatively, many poorly curated training sets containing noisy labels are readily available to be used to build new models. However, the successful exploration of such noisy-label training sets depends on the development of algorithms and models that are robust to these noisy labels. |
| Beschreibung: | Includes bibliographical references and index. - Description based on print version record |
| Umfang: | 1 Online-Ressource. |
| ISBN: | 9780443154423 0443154422 |
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| spelling | Carneiro, Gustavo VerfasserIn aut Machine learning with noisy labels definitions, theory, techniques, and solutions Gustavo Carneiro First edition. San Diego, CA Academic Press [2024] 1 Online-Ressource. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on print version record Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling 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. Alternatively, many poorly curated training sets containing noisy labels are readily available to be used to build new models. However, the successful exploration of such noisy-label training sets depends on the development of algorithms and models that are robust to these noisy labels. Machine learning Apprentissage automatique 0443154414 Erscheint auch als Druck-Ausgabe 0443154414 |
| spellingShingle | Carneiro, Gustavo Machine learning with noisy labels definitions, theory, techniques, and solutions Machine learning Apprentissage automatique |
| title | Machine learning with noisy labels definitions, theory, techniques, and solutions |
| title_auth | Machine learning with noisy labels definitions, theory, techniques, and solutions |
| title_exact_search | Machine learning with noisy labels definitions, theory, techniques, and solutions |
| title_full | Machine learning with noisy labels definitions, theory, techniques, and solutions Gustavo Carneiro |
| title_fullStr | Machine learning with noisy labels definitions, theory, techniques, and solutions Gustavo Carneiro |
| title_full_unstemmed | Machine learning with noisy labels definitions, theory, techniques, and solutions Gustavo Carneiro |
| title_short | Machine learning with noisy labels |
| title_sort | machine learning with noisy labels definitions theory techniques and solutions |
| title_sub | definitions, theory, techniques, and solutions |
| topic | Machine learning Apprentissage automatique |
| topic_facet | Machine learning Apprentissage automatique |
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