Greedy Randomized Adaptive Search Procedures
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This chapter addresses the problem of designing and training artificial neural networks with discrete activation functions for the classification of patterns in two categories. When the set of patterns is not linearly separable, this problem consists of determining the amount of neurons in the hidden layer that are needed to correctly classify the patterns. This problem has been reported to be an NP-hard problem. In this case, a GRASP is proposed, which exploits the particular structure of the model to determine the neurons of the hidden layer of the network as well as their corresponding weights. This procedure adds neurons, one at a time, until there are no misclassified patterns. Then it is possible to apply the condition of linear separability in order to obtain the corresponding weights of the neuron of the output layer. As a result, a trained network is obtained, which correctly classifies all the patterns in the training set. The procedure is tested with ten benchmarks datasets and results show that it performs well in a reasonable amount of time.
Key wordsClassification problem neural networks GRASP constructive procedure
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