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Learning Classifiers from Large Databases Using Statistical Queries

We describe an approach to learning predictive models from large databases in settings where direct access to data is not available because of massive size of data, access restrictions, or bandwidth requirements. We outline some techniques for minimizing the number of statistical queries needed; and for efficiently coping with missing values in the data. We provide open source implementation of the decision tree and naive Bayes algorithms to demonstrate the feasibility of the proposed approach.