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ICML
2001
IEEE

Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation

10 years 4 months ago
Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation
This work is concerned with the estimation of a classifier's accuracy. We first review some existing methods for error estimation, focusing on cross-validation and bootstrap, and motivate the use of kernel-based smoothing for small sample size. We use the term data cloning to refer to the process of (re)sampling the data via kernel-based smoothed bootstrap. A number of novel estimators based on cloning is presented. Finally, we extend our estimators to to allow cloning of complex real-life data sets, in which a data point may include continuous, bounded, integer and nominal attributes. This allows for better 1
Gregory Shakhnarovich, Ran El-Yaniv, Yoram Baram
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2001
Where ICML
Authors Gregory Shakhnarovich, Ran El-Yaniv, Yoram Baram
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