Instance‐based prediction of real‐valued attributes

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Abstract
Instance‐based representations have been applied to numerous classification tasks with some success. Most of these applications involved predicting a symbolic class based on observed attributes. This paper presents an instance‐based method for predicting a numeric value based on observed attributes. We prove that, given enough instances, if the numeric values are generated by continuous functions with bounded slope, then the predicted values are accurate approximations of the actual values. We demonstrate the utility of this approach by comparing it with a standard approach for value prediction. The instance‐based approach requires neither ad hoc parameters nor background knowledge.

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