Ensemble Methods: Foundations and Algorithms
Users: 1 - Average Rating: 4.00
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.
Type:
Book
Area:
Machine Learning
Target Group:
Basic
DOI:
https://dl.acm.org/doi/10.5555/2381019
Cite as:
Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms (1st. ed.). Chapman & Hall/CRC (2012)
Author of the review:
Ivo Nowak
HAW Hamburg
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Giulia Cademartori
This book is a good reading for people interested in ensemble methods from different perspectives - industrial and research. It provides an in-depth review of robust ensemble techniques with both theoretical and empirical analysis.