This paper extends the currently accepted model of inductive bias by identifying six categories of
bias and separates inductive bias from the policy for its selection (the inductive policy). We analyze existing
“bias selection” systems, examining the similarities and differences in their inductive policies, and identify three
techniques useful for building inductive policies. We then present a framework for representing and automatically
selecting a wide variety of biases and describe experiments with an instantiation of the framework addressing
various pragmatic trade offs of time, space, accuracy, and the cost of errors. The experiments show that a common
framework can be used to implement policies for a variety of different types of bias selection, such as parameter
selection, term selection, and example selection, using similar techniques. The experiments also show that different
trade offs can be made by the implementation of different policies; for example, from the same data different rule
sets can be learned based on different trade offs of accuracy versus the cost of erroneous predictions.
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