Actions for Machine Learning Methods with Noisy, Incomplete or Small Datasets

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Machine Learning Methods with Noisy, Incomplete or Small Datasets

Published
Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Physical Description
1 online resource (316 p.)
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Language Note
English
Restrictions on Access
Open Access Unrestricted online access
Summary
In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios.
Subject(s)
Other Subject(s)
ISBN
9783036512877
9783036512884
books978-3-0365-1287-7
Collection
DOAB Library.
Terms of Use and Reproduction
Creative Commons https://creativecommons.org/licenses/by/4.0/
View MARC record | catkey: 43682966