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020 _a9789819970070
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024 7 _a10.1007/978-981-99-7007-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aOnline Machine Learning
_h[electronic resource] :
_bA Practical Guide with Examples in Python /
_cedited by Eva Bartz, Thomas Bartz-Beielstein.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIII, 155 p. 49 illus., 38 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
505 0 _aChapter 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.
520 _aThis 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.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
700 1 _aBartz, Eva.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBartz-Beielstein, Thomas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819970063
776 0 8 _iPrinted edition:
_z9789819970087
776 0 8 _iPrinted edition:
_z9789819970094
830 0 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
856 4 0 _uhttps://doi.org/10.1007/978-981-99-7007-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c187325
_d187325