Fig. 2From: High-dimensional neural feature design for layer-wise reduction of training costTraining and testing accuracy against size of one instance of HNF using ELM feature vector in the first layer. Size of an L-layer HNF is represented by the number of random matrix-based nodes, counted as \(n^{(1)} + \sum _{l=2}^{L} 2n^{(l)}\). Here, L=3 for all three datasets. The number of nodes in the first layer (n(1)) is set according to Table 1 for each datasetBack to article page