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30 August 2005 Small-sample error estimation: mythology versus mathematics
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Abstract
Error estimation is a key aspect of statistical pattern recognition. The true classification error rate is usually unavailable since it depends on the unknown feature-label distribution. Hence, one needs to estimate the error rate from the available sample data. This paper presents a concise, mathematically rigorous review of the subject of error estimation in statistical pattern recognition, pointing to the pitfalls that arise in small-sample settings due to the use of "rules of thumb" and a neglect for proper mathematical understanding of the problem.
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Ulisses Braga-Neto "Small-sample error estimation: mythology versus mathematics", Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 59160V (30 August 2005); https://doi.org/10.1117/12.619331
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