We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boosting algorithms are closely related to models of mislabeling in which the label is erroneously exchanged for others. For the two boosting algorithms, theoretical aspects supportin...

The AdaBoost like algorithm for boosting CART regression trees is considered. The boosting predictors sequence is analyzed on various data sets and the behaviour of the algorithm is investigated. An instability index of a given estimation method with respect to some training sample is defined. Based on the bagging algorithm, this instability index is then extended to quantify the additional ins...

In this paper, we investigate the theoretical and empirical properties of L2 boosting with kernel regression estimates as weak learners. We show that each step of L2 boosting reduces the bias of the estimate by two orders of magnitude, while it does not deteriorate the order of the variance. We illustrate the theoretical findings by some simulated examples. Also, we demonstrate that L2 boosting...

Classiier committee learning approaches have demonstrated great success in increasing the prediction accuracy of classiier learning , which is a key technique for datamining. These approaches generate several classiiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the nal classiication. It has been shown that Boosting and ...

Breiman’s bagging and Freund and Schapire’s boosting are recent methods for improving the predictive power of classifier learning systems. Both form a set of classifiers that are combined by voting, bagging by generating replicated bootstrap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that l...

The firing evoked by injection of simulated barrages of EPSCs into the proximal dendrite of layer 5 pyramidal neurons is greater than when comparable inputs are injected into the soma. This boosting is mediated by dendritic Na+ conductances. However, the presence of other active conductances in the dendrites, some of which are nonuniformly distributed, suggests that the degree of boosting may d...

Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks, we analyze gradient-based descent algorithms for boosting with respect to any convex objective and introduce a new measure of weak learner performance into this setting which generalizes existing work. We present the ...

Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classiier learning systems. Both form a set of classiiers that are combined by voting, bagging by generating replicated boot-strap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that le...

Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, i...

Breiman s bagging and Freund and Schapire s boosting are recent methods for improving the predictive power of classi er learning systems Both form a set of classi ers that are combined by voting bagging by generating replicated boot strap samples of the data and boosting by ad justing the weights of training instances This paper reports results of applying both techniques to a system that learn...