Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in dataanalytics due to its universal approximation capability and fast modeling property. The technical...
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Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in dataanalytics due to its universal approximation capability and fast modeling property. The technical essence lies in stochastically configuring the hidden nodes (or basis functions) based on a supervisory mechanism rather than data-independent randomization as usually adopted for building randomized neural networks. Given imagedata modeling tasks, the use of 1-D SCNs potentially demolishes the spatial information of images, and may result in undesirable performance. This paper extends the original SCNs to a 2-D version, called 2DSCNs, for fast building randomized learners with matrix inputs. Some theoretical analysis 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 example, four benchmark handwritten digit classification tasks, two human face recognition datasets, as well as one natural imagedatabase, demonstrate that the proposed 2DSCNs perform favorably and show good potential for image data analytics.
Deep stochastic configuration networks (DeepSCNs), as a kind of randomized learner model, have the capability to generate a learning representation quickly and efficiently. Based on DeepSCNs, convolutional stochastic ...
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Deep stochastic configuration networks (DeepSCNs), as a kind of randomized learner model, have the capability to generate a learning representation quickly and efficiently. Based on DeepSCNs, convolutional stochastic configuration networks (ConSCNs) demonstrated superiority in spectroscopic dataanalytics by utilizing 1D convolutional and pooling operations. However, directly employing 1D ConSCNs in image data analytics could potentially lead to the loss of spatial information in images and poor generalization performance. This paper extends the original ConSCNs to a 2D version, termed 2DConSCNs, aiming at rapidly constructing randomized learners with 2D input shapes. Empirical results on eight benchmark datasets demonstrate the proposed 2DConSCNs outperform several existing randomized learner models and show good potential for image data analytics.
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