Machine Learning with Noisy Labels : Definitions, Theory, Techniques and Solutions.

Format:

Book

Author/Creator:

Carneiro, Gustavo.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (314 pages)

Edition:

1st ed.

Place of Publication:

San Diego : Elsevier Science & Technology, 2024.

Summary:

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.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Carneiro, Gustavo Machine Learning with Noisy Labels

ISBN:

9780443154423

9780443154416

OCLC:

1443189834