A New Learning Algorithm Using Simultaneous Perturbation with Weight Initialization
Neural Processing Letters
A Learning Based Widrow-Hoff Delta Algorithm for Noise Reduction in Biomedical Signals
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
A New Constructive Algorithm for Designing and Training Artificial Neural Networks
Neural Information Processing
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved three-term optical backpropagation algorithm
International Journal of Artificial Intelligence and Soft Computing
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Weight initialization in cascade-correlation learning is considered. Most of the previous studies use the so called candidate training to deal with the initialization problem in the cascade-correlation learning. There several candidate hidden units are first trained, and then the one yielding the best value for the covariance criterion is installed to the network. In case there are many candidate units to be trained, the total computational cost of the training can become very large. Here we consider a new approach for weight initialization in cascade-correlation learning. The proposed method is based on the concept of stepwise regression. Empirical simulations show that the new method can significantly speed-up cascade-correlation learning compared to the case where the candidate training is used. Moreover, the overall performance remained similar or was even better than with the candidate training