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Hi, I’d appreciate your thoughts regarding the following. I’m working with an insurance data set with the objective of predicting a binary outcome (claim or not). Policyholders in the sample are not observed for equivalent time periods. I have an exposure variable that reflects the amount of time each individual has been observed. In traditional GLM models, the usual way to handle this is to use the exposure as an offset variable (i.e. the coefficient for this variable is fixed at 1). I would like to extend the class of models I can use to model this data, using more recent techniques such as Support Vector Machines, Neural Nets, etc. But my question is how I can include the exposure information in the model in the way I do with GLM. I’m especially interested in SVM. Any thoughts are much appreciated. Regards, Lars.