Minimal model complexity search
SURFER is an empirical discovery system that given a set of input data and a modelling vocabulary returns the model that best describes that data. The best model is considered to be the one that minimizes the description length of that model plus the data encoded using that model. The search for models is controlled by the a priori estimate of model likelihoods as encoded in the modelling vocabulary. SURFER includes domain independent mechanisms for identifying redundant models and for finding free parameters. The system is described together with the results of running the system on several different types of problems.
KeywordsBayesian Learning discovery explanation-based learning minimum description length
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