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Machine learning algorithm validation with a limited sample size

Published:2019-11-07 Issue:11 Volume:14 Page:e0224365
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Vabalas Andrius^ORCID,Gowen Emma,Poliakoff Ellen,Casson Alexander J.

Funder

UK Engineering and Physical Sciences Research Council and its Doctoral Training Partnership with the University of Manchester

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference29 articles.

1. Small sample size effects in statistical pattern recognition: recommendations for practitioners;SJ Raudys;IEEE Transactions on Pattern Analysis and Machine Intelligence,1991

2. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age;C Sudlow;PLoS Medicine,2015

3. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls;MR Arbabshirani;NeuroImage,2016

4. Cross-validation failure: Small sample sizes lead to large error bars;G Varoquaux;NeuroImage,2018

5. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy;E Combrisson;Journal of Neuroscience Methods,2015

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