ENSEMBLE LEARNING

This updated 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 comprised of new materials highlighting descriptions of the classic methods and extensions and novel approaches that have recently been introducedAlong with algorithmic descriptions of each method the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured R code for implementation of the algorithm is also emphasizedThe unique volume provides researchers students and practitioners in industry with a comprehensive concise and convenient resource on ensemble learning methods