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• W2075637563 abstract "In this article, a Meta-cognitive Fully Complex-valued Fast Learning Classifier (Mc-FCFLC) for solving real-valued classification problems is presented. Mc-FCFLC consist of two components namely, a cognitive component and a meta-cognitive one. The cognitive component of Mc-FCFLC is a single hidden layer network (FCFLC) with a nonlinear input and hidden layer, and a linear output layer. The meta-cognitive component of Mc-FCFLC consist of a self-regulatory learning mechanism that chooses a best learning strategy among what-to-learn, when-to-learn and how-to-learn for a given sample. The sample is either deleted, used for adding a new neuron or else it is reserved for future use. Thus the architecture of Mc-FCFLC is constructed during the training process. The performance of the Mc-FCFLC is evaluated with the other complex-valued and a few best performing real-valued classifiers on a set of benchmark classification problems obtained from the UCI machine learning repository. Further, a practical acoustic emission signal classification problem has been addressed. Performance results demonstrate that Mc-FCFLC has better classification ability than the other classifiers existing in the literature." @default.
• W2075637563 created "2016-06-24" @default.
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• W2075637563 date "2015-03-01" @default.
• W2075637563 modified "2023-09-24" @default.
• W2075637563 title "A Meta-cognitive Fully Complex-valued Fast Learning Classifier for real-valued classification problems" @default.
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• W2075637563 doi "https://doi.org/10.1109/ccip.2015.7100712" @default.
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