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| 001 | 978-981-99-7007-0 | ||
| 003 | DE-He213 | ||
| 005 | 20240730171227.0 | ||
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| 008 | 240205s2024 si | s |||| 0|eng d | ||
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_a10.1007/978-981-99-7007-0 _2doi |
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_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. |
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| 300 |
_aXIII, 155 p. 49 illus., 38 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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| 490 | 1 |
_aMachine Learning: Foundations, Methodologies, and Applications, _x2730-9916 |
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| 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. _93407 |
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| 650 | 0 |
_aMachine learning. _91831 |
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| 650 | 1 | 4 |
_aArtificial Intelligence. _93407 |
| 650 | 2 | 4 |
_aMachine Learning. _91831 |
| 700 | 1 |
_aBartz, Eva. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _997625 |
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| 700 | 1 |
_aBartz-Beielstein, Thomas. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _997627 |
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_aSpringerLink (Online service) _997630 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-99-7007-0 |
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