Proceedings of the Annual Meeting of the Cognitive Science Society

The learning rate and convergence of connectionist learning algorithms are often dependent on their parameters. Most algorithms, if their parameters have been optimized at all, have been optimized by hand. This leads to absolute and relative performance problems. In absolute terms, researchers may not be getting optima] performance from their networks. In relative terms, comparisons of unoptimized or hand optimized algorithms may not be fair. (Sometimes even one is optimized and the other not.) This paper reports data suggesting that comparisons done in this manner are suspect. An example algorithm is presented that finds better parameter sets more quickly and fairly. Use of this algorithm (or similar techniques) would improve performance in absolute terms, provide fair comparisons between algorithms, and encourage the inclusion of parameter set behavior in algorithmic comparisons.