SMEM algorithm for mixture regression problems
We investigate applications of the SMEM (Split and Merge EM) algorithm to regression problems. We use the NGnet (Normalized Gaussian network) as a mixture regression model in which a set of input and output variables is dealt with as a joint probability. We describe the split and merge operations and criteria necessary for applying the SMEM algorithm to the NGnet. We show the usefulness of the SMEM algorithm for regression problems through experiments using artificial and real data. © 2001 Scripta Technica, Electron Comm Jpn Pt 2, 84(12): 54–62, 2001