364 Pages
43 Color & 27 B/W Illustrations
by
Chapman & Hall
364 Pages
43 Color & 27 B/W Illustrations
by
Chapman & Hall
Also available as eBook on:
Ensemble methods that train multiple learners and then combine them to use, with Boosting and Bagging as representatives, are well-known machine learning approaches. It has become common sense that an ensemble is usually significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.
Twelve years have passed since the... Read more
Preface
Notations
- Introduction
- Boosting
- Bagging
- Combination Methods
- Diversity
- Ensemble Pruning
- Clustering Ensemble
- Anomaly Detection and Isolation Forest
- Semi-Supervised Ensemble
- Class-Imbalance and Cost-Sensitive Ensemble
- Deep Learning and Deep Forest
- Advanced Topics
References
Index
Biography
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.
