Zhi-Hua Zhou

Ensemble Methods (eBook, PDF)

Foundations and Algorithms

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 364
• Erscheinungstermin: 15. Februar 2025
• Englisch
• ISBN-13: 9781040307632
• Artikelnr.: 72800911

Autorenporträt

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.

Inhaltsangabe

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