Abstract

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 "blas selection " systems, examining the similarities and differences in their inductive policies, and idemify three techniques useful for building inductive policies. We then present a framework for representing and automaticaIly selecting a wide variety of biases and describe experiments with an instantiation of the framework addressing various pragmatic tradeoffs of time, space, accuracy, and the cost oferrors. The experiments show that a common framework can be used to implement policies for a variety of different types of blas selection, such as parameter selection, term selection, and example selection, using similar techniques. The experiments also show that different tradeoffs can be made by the implementation of different policies; for example, from the same data different rule sets can be learned based on different tradeoffs of accuracy versus the cost of erroneous predictions.

Citations