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Titlebook: Online Machine Learning; A Practical Guide wi Eva Bartz,Thomas Bartz-Beielstein Book 2024 The Editor(s) (if applicable) and The Author(s),

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书目名称Online Machine Learning
副标题A Practical Guide wi
编辑Eva Bartz,Thomas Bartz-Beielstein
视频video
概述Presents systematic comparison of OML and BML in terms of performance, time and memory requirements.Explains how OML can be customized by hyperparameter tuning.Accompanied with continuously-updated co
丛书名称Machine Learning: Foundations, Methodologies, and Applications
图书封面Titlebook: Online Machine Learning; A Practical Guide wi Eva Bartz,Thomas Bartz-Beielstein Book 2024 The Editor(s) (if applicable) and The Author(s),
描述. .This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical considerations, and the third part substantiates them with concrete practical applications...The book is equally suitable as a reference manual for experts dealing with OML, as a textbook for beginners who want to deal with OML, and as a scientific publication for scientists dealing with OML since it reflects the latest state of research. But it can also serve as quasi OML consulting since decision-makers and practitioners can use the explanations to tailor OML to their needs and use it for their application and ask whether the benefits of OML might outweigh the costs...OML will soon become practical; it is worthwhile to get involved with it now. This book already presents some tools that will facilitate the practice of OML in the future. A promising breakthrough is expected because practice shows that due to the large amounts of dat
出版日期Book 2024
关键词Online Machine Learning; Machine Learning; Artificial Intelligence; Drift Detection; Supervised Learning
版次1
doihttps://doi.org/10.1007/978-981-99-7007-0
isbn_softcover978-981-99-7009-4
isbn_ebook978-981-99-7007-0Series ISSN 2730-9908 Series E-ISSN 2730-9916
issn_series 2730-9908
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Hyperparameter Tuning,(HPT) performed with the Sequential Parameter Optimization Toolbox (SPOT) is also important for the explainability and interpretability of OML procedures and can lead to a more efficient and thus resource-saving algorithm (“Green IT”).
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,Evaluation and Performance Measurement,ion . presents an implementation in Python for selecting training and test data. Section . describes the calculation of performance. Section . introduces the generation of benchmark data sets in the field of OML.
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,Special Requirements for Online Machine Learning Methods,or an extremely large number of variables (Sect. .). Section . describes important aspects such as fairness (Fair Machine Learning (ML)) or interpretability (Interpretable ML) in the context of OML algorithms.
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,Introduction: From Batch to Online Machine Learning,. This is especially true for available memory, handling drift in data streams, and processing new, unknown data. Online Machine Learning (OML) is an alternative to BML that overcomes the limitations of BML. In this chapter, the basic terms and concepts of OML are introduced and the differences to B
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