By Kenji Suzuki, editor
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Extra resources for Artificial neural networks - methodological advances and biomedical applications
Exhaustive evaluation of all of these possible combinations may be feasible when the dimensionality of the candidate set is low, but quickly becomes infeasible as dimensionality increases. 2 Forward selection Forward selection is a linear incremental search strategy that selects individual candidate variables one at a time. In the case of wrappers, the method starts by training d single-variable ANN models and selecting the input variable that maximises the model performance-based optimality criterion.
The immediately obvious effect of including a greater number of input variables is that the size of an ANN increases, which increases the computational burden associated with querying the network—a significant influence in determining the speed of training. In the case of the multilayer perceptron (MLP), the input layer will have an increased number of incoming connection weights. In the case of kernel-based generalised regression neural network (GRNN) and radial basis function (RBF) networks, the computation of distance to prototype vectors is more expensive due to higher dimensionality.
Due to the complexity of the ANN error surface that is projected over the weight-space, evolutionary algorithms have been found to be a good alternative to gradient descent algorithms. EAs are robust and able to more reliably find a near-globally optimum solution, even for highly non-linear functions, with many multiple local optima; whereas gradient descent algorithms have greater potential to converge prematurely at a local minimum. By applying penalty terms to avoid large weights, or by using a model sparsity term within the objective function, the optimisation can effectively determine the optimum trade-off between model error and model complexity during training (Tikka, 2008).
Artificial neural networks - methodological advances and biomedical applications by Kenji Suzuki, editor