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• W2081097129 abstract "In this paper, we consider a modified error-correction learning rule for the multilayer neural network with multivalued neurons (MLMVN). This modification is based on the soft margins technique, which leads to the minimization of the distance between a cluster center and the learning samples belonging to this cluster. MLMVN has a derivative-free learning algorithm based on the error-correction learning rule and demonstrate a higher functionality and better generalization capability than a number of other machine learning techniques. The discrete k-valued multivalued neuron activation function divides a complex plane into k equal sectors. For more efficient and reliable solving of classification problems it is possible to modify the MLMVN learning algorithm in such a way that learning samples belonging to different classes (clusters) will be located as close as possible to the bisector of a desired sector (the cluster center) and as far as possible from each other, respectively. Such a modification based on the soft margins learning technique is considered in this paper. This modified learning algorithm improves the generalization capability of MLMVN when solving classification problems." @default.
• W2081097129 created "2016-06-24" @default.
• W2081097129 creator A5052101553 @default.
• W2081097129 date "2014-09-01" @default.
• W2081097129 modified "2023-10-06" @default.
• W2081097129 title "MLMVN With Soft Margins Learning" @default.
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• W2081097129 doi "https://doi.org/10.1109/tnnls.2014.2301802" @default.
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