04023nam a22005415i 4500 978-981-99-7007-0 DE-He213 20251030111417.0 cr nn 008mamaa 240205s2024 si | s |||| 0|eng d 9789819970070 978-981-99-7007-0 10.1007/978-981-99-7007-0 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Online Machine Learning [electronic resource] : A Practical Guide with Examples in Python / edited by Eva Bartz, Thomas Bartz-Beielstein. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XIII, 155 p. 49 illus., 38 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 Chapter 1:Introduction -- Chapter 2:Supervised Learning -- Chapter 3:Drift Detection and Handling -- Chapter 4:Initial Selection and Subsequent Updating of OML Models -- Chapter 5:Evaluation and Performance Measurement -- Chapter 6:Special Requirements for OML Methods -- Chapter 7:Practical Applications of Online Machine Learning -- Chapter 8:Open-Source-Software for Online Machine Learning -- Chapter 9:An Experimental Comparison of Batch and Online Machine Learning Algorithms -- Chapter 10:Hyperparameter Tuning -- Chapter 11:Summary and Outlook. 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 data that accumulate, the previous BML is no longer sufficient. OML is the solution to evaluate and process data streams in real-time and deliver results that are relevant for practice. Artificial intelligence. 58441 Machine learning. 55059 Artificial Intelligence. 58441 Machine Learning. 55059 Bartz, Eva. editor. edt http://id.loc.gov/vocabulary/relators/edt 70644 Bartz-Beielstein, Thomas. editor. edt http://id.loc.gov/vocabulary/relators/edt 70645 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819970063 Printed edition: 9789819970087 Printed edition: 9789819970094 Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 70646 https://doi.org/10.1007/978-981-99-7007-0 ZDB-2-SCS ZDB-2-SXCS 57501 57501