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• W2891533552 abstract "Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and fast modelling property. The technical essence lies in stochastically configuring hidden nodes (or basis functions) based on a supervisory mechanism rather than data-independent randomization as usually adopted for building randomized neural networks. Given image data modelling tasks, the use of one-dimensional SCNs potentially demolishes the spatial information of images, and may result in undesirable performance. This paper extends the original SCNs to two-dimensional version, termed 2DSCNs, for fast building randomized learners with matrix-inputs. Some theoretical analyses on the goodness of 2DSCNs against SCNs, including the complexity of the random parameter space, and the superiority of generalization, are presented. Empirical results over one regression, four benchmark handwritten digits classification, and two human face recognition datasets demonstrate that the proposed 2DSCNs perform favourably and show good potential for image data analytics." @default.
• W2891533552 created "2018-09-27" @default.
• W2891533552 creator A5021272598 @default.
• W2891533552 creator A5021384155 @default.
• W2891533552 date "2018-09-06" @default.
• W2891533552 modified "2023-09-27" @default.
• W2891533552 title "Two Dimensional Stochastic Configuration Networks for Image Data Analytics." @default.
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