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Multiple classifier systems or ensemble is an idea that is relevant both to neural computing and to machine learning community. Different MCSs can be designed for creating classifier ensembles with different combination functions. However, the best MCS can only be determined by performance evaluation. In this study, MCS is used to construct discriminant set that was used to discriminate the difficult...
This paper proposes a multi-classification pattern algorithm using multilayer perceptron neural network models which try to boost two conflicting main objectives of a classifier, a high correct classification rate and a high classification rate for each class. To solve this machine learning problem, we consider a memetic Pareto evolutionary approach based on the NSGA2 algorithm (MPENSGA2), where we...
Generalization error is very important in machine learning and pattern classification. However, one can not compute the generalization error for a given problem exactly. Therefore, many research efforts have been put to estimate the generalization error for a given classification problem. The localized generalization error model (L-GEM) is one of the recently proposed analytical generalization error...
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