Experiments with the cascade-correlation algorithm

This paper describes a series of experiments with the cascade-correlation algorithm (CCA) and some of its variants on a number of real-world pattern classification tasks. Some of these experiments investigate the effect of different design parameters on the performance of CCA (in terms of number of training epochs and classification accuracy on the test data). Parameter settings that consistently yield good performance on different data sets are identified. The performance of CCA is compared with that of the backpropagation algorithm (BP) and the perceptron algorithm (PA). Preliminary results obtained from some variants of CCA and some directions for future work with CCA-like generative algorithms for neural networks are discussed.