A method for using probabilistic background knowledge to select features for representing learned concepts is described. The method uses a model of the tradeoff between accuracy (predictiveness) and simplicity (size) of hypothesis spaces. Preliminary results of using various biases for a learning task in a randomized test domain are discussed.
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