On the use of a pruning prior for neural networks

We address the problem of using a regularization prior that prunes unnecessary weights in a neural network architecture. This prior provides a convenient alternative to traditional weight-decay. Two examples are studied to support this method and illustrate its use. First we use the sunspots benchmark problem as an example of time series processing. Then we address the problem of system identification on a small artificial system.