How do Neural Networks Compare with Statistical Techniques?
Neural network supervised learning corresponds to statistical non-linear discriminant analysis; neural network unsupervised learning corresponds to statistical clustering and factor analysis. Instead of executing complex statistical procedures — the neural network learning model iteratively converges to a pattern recognition function that is a optimal in a least-mean-square sense. The iterative process implicitly estimates the statistics of the historical training data. This parallels human pattern recognition: people learn patterns over time.
