Limits on Learning Machine Accuracy Imposed by Data Quality

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

Neural Information Processing Systems 

Random errors and insufficiencies in databases limit the performance ofany classifier trained from and applied to the database. In this paper we propose a method to estimate the limiting performance ofclassifiers imposed by the database. We demonstrate this technique on the task of predicting failure in telecommunication paths. 1 Introduction Data collection for a classification or regression task is prone to random errors, e.g.

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