Fast probabilistic self-structuring of generalized single-layer networks

An algorithm is presented for determining the subset of the basis functions of a generalized single-layer network (GSLN) needed to solve the classification problem defined by the training data. A Markov chain Monte Carlo sampling technique is used to traverse the space of models having a low sum squared error (SSE). The frequency of a term´s inclusion is an indication of its importance to the classifier. Fast, iterative updates can be used for the matrix calculations needed. Theoretical results for the required length of the chain needed to obtain good discrimination between functions fitting the data and those modeling the added noise are given, and these are confirmed by experiment