A constructive learning algorithm for an HME
Kazumi Saito, Ryoko Nakano · 2002
A hierarchical mixtures of experts (HME) model has been applied to several classes of problems, and its usefulness has been shown. However, defining an adequate structure in advance is required and the resulting performance depends on the structure. To overcome this problem, a constructive learning algorithm for an HME is proposed; it includes an initialization method, a training method and an extension method. In our experiments, which used parity problems and a function approximation problem, the proposed algorithm worked much better than the conventional method.