results before boosting, among 176 boosted individuals, 75 (42.6%) had hbsab 10 iu/l and were considered seroprotected. among 101 serosusceptible persons, more than 80% of boosted individuals showed more than twofold increase in cytokines concentration, which meant positive hbsag-specific cell-mediated immunity. mcl of both cytokines after boosting in giv were decreased more than twofold, possi...

A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...

We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online learning algorithm with smooth convex loss f...

In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...

We construct a framework which allows an algorithm to turn the distributions produced by some boosting algorithms into polynomially smooth distributions (w.r.t. the PAC oracle’s distribution), with minimal performance loss. Further, we explore the case of Freund and Schapire’s AdaBoost algorithm, bounding its distributions to polynomially smooth. The main advantage of AdaBoost over other boosti...

Résumé. En classification supervisée, de nombreuses méthodes ensemblistes peuvent combiner plusieurs hypothèses de base afin de créer une règle de décision finale plus performante. Ainsi, il a été montré que des méthodes comme le bagging ou le boosting pouvaient se révéler intéressantes, tant dans la phase d’apprentissage qu’en généralisation. Dès lors, il est tentant de vouloir s’inspirer des ...