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Table 8 Model hyper-parameters

From: Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards

Method Citation Hyper-Parameters
Multi-layer perceptron network (MLP) [55, 57] Number of hidden layers: 3
Optimization method: scaled conjugate gradient backpropagation, learning rate self-adapting
Neurons per hidden layer: 50, 25, 10
Loss function: mean-squared error
Radial-basis function network with relevance (rRBF) [58,59,60] Number of radial basis functions: 30
Optimization method: scaled non-linear conjugate gradient, learning rate self adapting
Loss function: mean-squared error
Partially least square (PLS) [61] Number of components: 20
Linear discriminance model (LDA) [62] No hyper-parameters