Learning Mackey-Glass from 25 Examples, Plus or Minus 2

We apply active exemplar selection (Plutowski & White, 1991; 1993) to predicting a chaotic time series. Given a fixed set of examples, the method chooses a concise subset for training. Fitting these exemplars results in the entire set being fit as well as desired. The algorithm incorporates a method for regulating network complexity, automatically adding exemplars and hidden units as needed. Fitting examples generated from the Mackey-Glass equation with fractal dimension 2.1 to an rmse of 0.01 required about 25 exemplars and 3 to 6 hidden units. The method requires an order of magnitude fewer floating point operations than training on the entire set of examples, is significantly cheaper than two contending exemplar selection techniques, and suggests a simpler active selection technique that performs comparably.