Experiments with the cascade-correlation algorithm

A series of experiments with the cascade-correlation algorithm (CCA) and some of its variants on a number of real-world pattern classification tasks are described. Some of the experiments investigated the effect of different design parameters on the performance of the CCA. Parameter settings that consistently yield good performance on different data sets were identified. The performance of the CCA is compared with that of the backpropagation algorithm and the perceptron algorithm. Preliminary results obtained from some variants of CCA and some directions for future work with CCA-like neural network learning methods are discussed