An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures.

https://dl.acm.org/doi/10.5555/2381019

Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms (1st. ed.). Chapman & Hall/CRC (2012)

Ivo Nowak

HAW Hamburg