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Limits on Learning Machine Accuracy Imposed by Data Quality

Corinna Cortes^a, L. D. Jackel^a, Wan-Ping Chiang^a

^aAT&T Bell Laboratories, holmdel, NJ

1994-01-01 89 citations10 references

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Abstract

Random errors and insufficiencies in databases limit the perfor-mance of any classifier trained from and applied to the database. In this paper we propose a method to estimate the limiting perfor-mance of classifiers imposed by the database. We demonstrate this technique on the task of predicting failure in telecommunication paths. 1

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Limits on Learning Machine Accuracy Imposed by Data Quality

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