Neural and adaptive systems : fundamentals through simulations

By José Carlos Príncipe, N. Euliano, W. C. Lefebvre. 2000

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Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks (2010, background): “where Zt is the actual output of the t-th residue in the training dataset, Dt is the target output vector (the associated class of Zt), and P is the number of training patterns [25].”
Washback to learning outcomes: a comparative study of IELTS preparation and university pre‐sessional language courses (2007, methodology): “An artificial intelligence application was chosen for this analysis: a neural network developed using NeuroSolutions 4.16 (Principe et al., 2000).”
Information Theoretic Learning (2005, background): “…can be derived for Gaussian kernels of standard deviation σ on an iid sample set { x 1 N } as the sum of pairwise sample (particle) interactions (Principe et al, 2000): (2) The pairwise interaction of samples through the kernel intriguingly provides a connection to entropy of particles in…”