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Beschreibung
Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. An ensemble is significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.
Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. An ensemble is significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.
Über den Autor

Zhi-Hua Zhou, Professor of Computer Science and Artificial Intelligence at Nanjing University, President of IJCAI trustee, Fellow of the ACM, AAAI, AAAS, IEEE, recipient of the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, CCF-ACM Artificial Intelligence Award.

Inhaltsverzeichnis

Preface Notations 1. Introduction 2. Boosting 3. Bagging 4. Combination Methods 5. Diversity 6. Ensemble Pruning 7. Clustering Ensemble 8. Anomaly Detection and Isolation Forest 9. Semi-Supervised Ensemble 10. Class-Imbalance and Cost-Sensitive Ensemble 11. Deep Learning and Deep Forest 12. Advanced Topics References Index

Details
Erscheinungsjahr: 2025
Fachbereich: Allgemeines
Genre: Importe, Wirtschaft
Rubrik: Recht & Wirtschaft
Medium: Buch
Inhalt: Einband - fest (Hardcover)
ISBN-13: 9781032960609
ISBN-10: 1032960604
Sprache: Englisch
Einband: Gebunden
Autor: Zhou, Zhi-Hua
Auflage: 2. Auflage
Hersteller: Chapman and Hall/CRC
Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, D-80331 München, gpsr@taylorandfrancis.com
Maße: 240 x 161 x 24 mm
Von/Mit: Zhi-Hua Zhou
Erscheinungsdatum: 09.03.2025
Gewicht: 0,71 kg
Artikel-ID: 130050469

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