Neural network pruning with Tukey-Kramer multiple comparison procedure
Neural Computation
Feedforward Neural Network Construction Using Cross Validation
Neural Computation
Variations of the two-spiral task
Connection Science
Using Three Layer Neural Network to Compute Multi-valued Functions
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Novel maximum-margin training algorithms for supervised neural networks
IEEE Transactions on Neural Networks
Comparing logistic regression, neural networks, c5.0 and m5′ classification techniques
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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A constructive neural-network algorithm is presented. For any consistent classification task on real-valued training vectors, the algorithm constructs a feedforward network with a single hidden layer of threshold units which implements the task. The algorithm, which we call CARVE, extends the “sequential learning” algorithm of Marchand et al. (1990) from Boolean inputs to the real-valued input case, and uses convex hull methods for the determination of the network weights. The algorithm is an efficient training scheme for producing near-minimal network solutions for arbitrary classification tasks. The algorithm is applied to a number of benchmark problems including German and Sejnowski's sonar data, the Monks problems and Fisher's iris data. A significant application of the constructive algorithm is in providing an initial network topology and initial weights for other neural-network training schemes, and this is demonstrated by application to backpropagation