Machine Learning Methods with Noisy, Incomplete or Small Datasets

ISBN 10: 3036512888 / ISBN 13: 9783036512884

Published by MDPI AG, 2021

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9783036512884

Bibliographic Details

• Title: Machine Learning Methods with Noisy, Incomplete or Small Datasets
• Publisher: MDPI AG
• Publication Date: 2021
• Language: English
• ISBN 10: 3036512888
• ISBN 13: 9783036512884
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 25D millimeters 250 millimeters width by 175 millimeters height by 25 millimeters depth
• Weight: 990 grams

About this title

Synopsis

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.