On the computation of distribution-free performance bounds: Application to small sample sizes in neuroimaging
作者：
Highlights：
• Practical and novel upper bounds for the resubstitution error estimate are derived.
• Based on classical combinatorial geometry with connection to Vapnik’s theory.
• Experiments on synthetic and neuroimaging data demonstrate the performance of resubstitution error estimators.
• Under heterogeneous scenarios their performance is similar or greater to that obtained by cross-validation method.
摘要
•Practical and novel upper bounds for the resubstitution error estimate are derived.•Based on classical combinatorial geometry with connection to Vapnik’s theory.•Experiments on synthetic and neuroimaging data demonstrate the performance of resubstitution error estimators.•Under heterogeneous scenarios their performance is similar or greater to that obtained by cross-validation method.
论文关键词：Resubsitution error estimate,Lineal classifiers,Upper bounds,Neuroimaging,VC dimension
论文评审过程：Received 9 October 2018, Revised 13 February 2019, Accepted 30 March 2019, Available online 4 April 2019, Version of Record 11 April 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.03.032
