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• W2010219744 abstract "In this paper, a learning model for binary and multiclass classification based on local modeling and Information Theoretic Learning (ITL) is described. The training algorithm for the model works on two stages: first, a set of nodes are placed on the frontiers between classes using a modified clustering algorithm based on ITL. Each of these nodes defines a local model. Second, several one-layer neural networks, associated with these local models, are trained to locally classify the points in its proximity. The method is successfully applied to problems with a large amount of instances and high dimension like intrusion detection and microarray gene expression." @default.
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• W2010219744 date "2012-10-16" @default.
• W2010219744 modified "2023-09-26" @default.
• W2010219744 title "Information Theoretic Learning and local modeling for binary and multiclass classification" @default.
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• W2010219744 doi "https://doi.org/10.1007/s13748-012-0032-8" @default.
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