In this paper we propose a new approach for dynamic selection of ensembles of classifiers.
Based on the concept named multistage organizations, the main objective of which is to …

We present an evaluation of incremental learning algorithms for the estimation of hidden
Markov model (HMM) parameters. The main goal is to investigate incremental learning …

The overproduce-and-choose sttategy is a static classifier ensemble selection approach,
which is divided into overproduction and selection phases. This thesis focuses on the …

Durant ces dernières années, les machines à vecteurs de support (SVM) ont démontré
maintes reprises leur supériorité en termes de généralisation. L'objectif de cette thèse de …

The optimization of many engineering systems is challenged by the solution over-fit to the
data set used to evaluate potential solutions during the evolutionary process. The solution …

This thesis focuses on the design of adaptive systems (AS) for dealing with complex pattern
recognition problems. Pattern recognition systems usually rely on static knowledge to define …

Anomaly detection monitors for significant deviations from normal system behavior. Hidden
Markov Models (HMMs) have been successfully applied in many intrusion detection …