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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.
In addition to this book, interactive Jupyter Notebooks and further material about OML are provided in the GitHub repository [...] The repository is continuously maintained, so the notebooks may change over time.
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.
In addition to this book, interactive Jupyter Notebooks and further material about OML are provided in the GitHub repository [...] The repository is continuously maintained, so the notebooks may change over time.
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.
Erscheinungsjahr: | 2024 |
---|---|
Genre: | Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Seiten: | 172 |
Reihe: | Machine Learning: Foundations, Methodologies, and Applications |
Inhalt: |
xiii
155 S. 11 s/w Illustr. 38 farbige Illustr. 155 p. 49 illus. 38 illus. in color. |
ISBN-13: | 9789819970063 |
ISBN-10: | 9819970067 |
Sprache: | Englisch |
Ausstattung / Beilage: | HC runder Rücken kaschiert |
Einband: | Gebunden |
Redaktion: |
Bartz-Beielstein, Thomas
Bartz, Eva |
Herausgeber: | Eva Bartz/Thomas Bartz-Beielstein |
Auflage: | 1st ed. 2024 |
Hersteller: |
Springer Singapore
Springer Nature Singapore Machine Learning: Foundations, Methodologies, and Applications |
Maße: | 241 x 160 x 16 mm |
Von/Mit: | Thomas Bartz-Beielstein (u. a.) |
Erscheinungsdatum: | 06.02.2024 |
Gewicht: | 0,43 kg |
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.
Erscheinungsjahr: | 2024 |
---|---|
Genre: | Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Seiten: | 172 |
Reihe: | Machine Learning: Foundations, Methodologies, and Applications |
Inhalt: |
xiii
155 S. 11 s/w Illustr. 38 farbige Illustr. 155 p. 49 illus. 38 illus. in color. |
ISBN-13: | 9789819970063 |
ISBN-10: | 9819970067 |
Sprache: | Englisch |
Ausstattung / Beilage: | HC runder Rücken kaschiert |
Einband: | Gebunden |
Redaktion: |
Bartz-Beielstein, Thomas
Bartz, Eva |
Herausgeber: | Eva Bartz/Thomas Bartz-Beielstein |
Auflage: | 1st ed. 2024 |
Hersteller: |
Springer Singapore
Springer Nature Singapore Machine Learning: Foundations, Methodologies, and Applications |
Maße: | 241 x 160 x 16 mm |
Von/Mit: | Thomas Bartz-Beielstein (u. a.) |
Erscheinungsdatum: | 06.02.2024 |
Gewicht: | 0,43 kg |