A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

We investigate the use of certain data-dependent estimates of the complexity of a function
class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we …

We propose a method for generating classifier ensembles based on feature extraction. To
create the training data for a base classifier, the feature set is randomly split into K subsets …

We provide an abstract characterization of boosting algorithms as gradient decsent on cost-
functionals in an inner-product function space. We prove convergence of these functional …

Recently ensemble methods like ADABOOST have been applied successfully in many
problems, while seemingly defying the problems of overfitting. ADABOOST rarely overfits in …

AdaBoost is one of the most excellent Boosting algorithms. It has a solid theoretical basis
and has made great success in practical applications. AdaBoost can boost a weak learning …

We prove new probabilistic upper bounds on generalization error of complex classifiers that
are combinations of simple classifiers. Such combinations could be implemented by neural …

We show how the concave-convex procedure can be applied to transductive SVMs, which
traditionally require solving a combinatorial search problem. This provides for the first time a …

Diversity among the base classifiers is deemed to be important when constructing a
classifier ensemble. Numerous algorithms have been proposed to construct a good …

We provide an introduction to theoretical and practical aspects of Boosting and Ensemble
learning, providing a useful reference for researchers in the field of Boosting as well as for …