Program Synthesis in the Presence of Infinite Number of Inaccuracies

Most studies modeling inaccurate data in Gold style learning consider cases in which the number of inaccuracies is finite. The present paper argues that this approach is not reasonable for modeling inaccuracies in concepts that are infinite in nature (for example, graphs of computable functions).

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