Ensembles of classifiers represent one of the main research directions in machine learning. Two main theories are invoked to explain the success of ensemble methods. The first one consider the ensembles in the framework of large margin classifiers, showing that ensembles enlarge the margins, enhancing the generalization capabilities of learning algorithms. The second is based on the classical b...

Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, e-GASEN, a twolayer neural network ensemble architecture is proposed, in which the base learners of the final ensemble are also ensembles. Experimental results show that e-GASEN generalizes better than a popular ensemble method. The reason why e-GASEN works is also di...

Ensembles of classifiers will produce lower errors than the member classifiers if there is diversity in the ensemble. One means of producing this diversity in nearest neighbour classifiers is to base the member classifiers on different feature subsets. In this paper we show four examples where this is the case. This has implications for the practice of feature subset selection (an important iss...

Importance Sampled Circuit Learning Ensembles (ISCLEs) is a novel analog circuit topology synthesis method that returns designertrustworthy circuits yet can apply to a broad range of circuit design problems including novel functionality. ISCLEs uses the machine learning technique of boosting, which does importance sampling of “weak learners” to create an overall circuit ensemble. In ISCLEs, the...

accurate quantitative precipitation forecasts (qpfs) have been always a demanding and challenging job in numerical weather prediction (nwp). the outputs of ensemble prediction systems (epss) in the form of probability forecasts provide a valuable tool for probabilistic quantitative precipitation forecasts (pqpfs). in this research, different configurations of wrf and mm5 meso-scale models form ...