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This work proposes a novel method for constructing RBF networks, based on boosting. The task assigned to the base learner is to select a RBF, while the boosting algorithm combines linearly the different RBFs. For each iteration of boosting a new neuron is incorporated into the network. The method for selecting each RBF is based on randomly selecting several examples as the centers, considering...
A supervised classification method for temporal series, even multivariate, is presented. It is based on boosting very simple classifiers, which consists only of one literal. The proposed predicates are based in similarity functions (i.e., euclidean and dynamic time warping) between time series. The experimental validation of the method has been done using different datasets, some of them obtained...
Consistency-based diagnosis automatically provides fault detection and localization capabilities, using just models for correct behavior. However, it may exhibit a lack of discrimination power. Knowledge about fault modes can be added to tackle the problem. Unfortunately, it brings additional complexity issues, since it will be necessary to discriminate among a maximum of KN mode assignments, for...
We present a method for constructing ensembles of classifiers using supervised projections of random subspaces. The method combines the philosophy of boosting, focusing on difficult instances, with the improved accuracy achieved by supervised projection methods to obtain very good results in terms of testing error. To achieve both accuracy and diversity, random subspaces are created at each step,...
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