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.
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Oleg Okun, Giorgio Valentini, Matteo Re. 10.2 Background of Ensemble Selection The Ensemble Selection ( also called Ensemble Pruning or Overproduce and Choose paradigm ) consists in selecting the ensemble members from a set of individual ...
Equal parts call to reason and to joy, this book is an irrepressible celebration of our oddball, interconnected world. The Everybody Ensemble delivers unexpected wisdom and a wake-up call that sounds from within.
... ensemble, that employs the same model (k-means clustering using cosine distance). Some additional benefits for clustering ensembles are: • Novelty, as the solution obtained by the ensemble is not reachable by single clus- tering methods ...
The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study.