The purpose of this study is to investigate a functional relation between item exposure parameters (IEPs) and item parameters (IPs) over parallel pools. This functional relation is approximated by a well-known tool in machine learning. Let P and Q be parallel item pools and suppose IEPs for P have been obtained via a Sympson and Hetter-type simulation. Based on these simulated parameters, a functional relation k = f[subscript P] (a, b, c) relating IPs to IEPs of P is obtained by an artificial neural network and used to estimate IEPs of Q without tedious simulation. Extensive experiments using real and synthetic pools showed that this approach worked pretty well for many variants of the Sympson and Hetter procedure. It worked excellently for the conditional Stocking and Lewis multinomial selection procedure and the Chen and Lei item exposure and test overlap control procedure. This study provides the first step in an alternative means to estimate IEPs without iterative simulation. (Contains 16 tables and 7 figures.)