Abstract
This chapter gives the latest training methods for training the weights and biases of feed-forward neural networks (FNNs). Note that the training is not pure optimization; hence, the training should be over the validation set. The traditional back propagation scheme, performed by the gradient descent method, and its variants, are reviewed. Later, 10 global optimization methods are compared, including the genetic algorithm, simulate annealing, the tabu search, the artificial immune system, particle swarm optimization, artificial bee colony, the firefly algorithm, ant colony optimization, biogeography-based optimization, and the Jaya algorithm.
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Wang, SH., Zhang, YD., Dong, Z., Phillips, P. (2018). Weight Optimization of Classifiers for Pathological Brain Detection. In: Pathological Brain Detection. Brain Informatics and Health. Springer, Singapore. https://doi.org/10.1007/978-981-10-4026-9_9
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