We introduce the programming language J and show its applicability for experimenting with neural networks and genetic algorithms. We illustrate the use of J with complete programs for perceptron and back propagation learning.
We develop a new approach to training encoder feedforward neural networks and apply it to two classes of problems. Our approach is to initially train the network with a related, relatively easy-to-learn problem, and then gradually replace the training set with harder problems, until the network learns the problem we originally intended to solve.
The problems we address are modifications of the common N-2-N encoder network problem with N exemplars, the unit vectors, e_k in N-space. Our first modification of the problem is to use objects consisting of paired 1's (e_k + e_(k+1)), with subscripts taken mod N). This requires an N-2-N net to organize the images of the exemplars in 2-space ordered around a circle. Our second modification is to use patterns consisting of two objects; each object is a pair of adjacent 1's; the objects must be separated from each other. This problem can be learned by a N-4-N network which must organize the images of the exemplars in 4-space in the form of a mobius strip.
The easy-to-learn problem in both cases involves replacing the the two-ones signal e_k + e_(k+1) with a block-signal of length B: e_k + e_(k+1) + ... + e_(k+B-1). In several cases, our method allowed us to train networks that otherwise fail to train. In some other cases, our method proved to be ten times faster than otherwise.
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