This up to date compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition constructed from new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that experience recently been introduced.
Along with algorithmic descriptions of each and every method, the settings through which each and every method is applicable and the results and tradeoffs incurred by the usage of the process is succinctly featured. R code for implementation of the set of rules could also be emphasized.
The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
- Introduction to Machine Learning
- Classification and Regression Trees
- Introduction to Ensemble Learning
- Ensemble Classification
- Gradient Boosting Machines
- Ensemble Diversity
- Ensemble Selection
- Error Correcting Output Codes
- Evaluating Ensembles of Classifiers
Readership: Professionals, researchers, academics, and graduate students in artificial intelligence, databases and machine learning.