Machine Learning Methods with Noisy, Incomplete or Small Datasets
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, i...
-д хадгалсан:
| Формат: | Online |
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| Хэл сонгох: | англи |
| Хэвлэсэн: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Нөхцлүүд: | |
| Онлайн хандалт: | ONIX_20220111_9783036512884_45 |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| Тойм: | 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. |
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