In this work, we explore data-driven techniques for the fast and early diagnosis concerning etiological origin of meningitis, more specifically with regard to differentiating between viral bacterial meningitis. We study how machine learning can be used predict meningitis aetiology once a patient has been diagnosed disease. have dataset 26,228 patients described by 19 attributes, mainly about pa...

In this paper we investigate a number of ensemble methods for improving the performance of phoneme classiication for use in a speech recognition system. We discuss boosting and mixtures of experts, both in isolation and in combination. We present results on an isolated word database. The results show that principled ensemble methods such as boosting and mixtures provide superior performance to ...

We present a series of learning algorithms and theoretical guarantees for designing accurate ensembles of structured prediction tasks. This includes several randomized and deterministic algorithms devised by converting on-line learning algorithms to batch ones, and a boosting-style algorithm applicable in the context of structured prediction with a large number of labels. We give a detailed stu...

Training an ensemble of networks is an interesting way to improve the performance with respect to a single network. However there are several method to construct the ensemble and there are no results showing which one could be the most appropriate. In this paper we present a comparison of eleven different method. We have trained ensembles of a reduced number of networks (3 and 9) because in thi...