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An extension to the workflow was subsequently added to support the CellNOptR model training function.
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The cost and gamma are parameters used in model training and kernel function of LIBSVM and need to be to tuned for optimal prediction results.
A history of previous prediction errors with a length of 30 samples is used for probability density function model training, which is done in each time interval.
In the first part of the study, different ANN models, comprising various combinations of training function and number of neutrons were developed to estimate the ET0 and it has been compared with the Penman Monteith (PM) ET0 as the ideal (observed) ET0.
Prior to model training, missing descriptor values were imputed (function ImputeFeatures).
For model training (Supplementary Section S1.12), the cost function used was C t) = 1 − A t)/0.01.
The registered spectra were mathematically treated following a linear (PLS-DA) approach and a non-linear one (ANN) based on multilayer perceptron models with different training functions.
Machine learning scoring functions consist of four main building blocks: descriptors, model, training set and test set.
Training function index = 4 bits.
The networks were trained using Bayesian regularisation backpropagation training function.
In this case, modeling was done using different training functions and different transfer functions for different neurons of hidden layers.
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