[web] Korea Internet & Security Agency / 2019 / Cyber Security Threat Trend Report / https://www.boho.or.kr

[book] Shai Ben-David / 2014 / Understanding Machine Learning: From Theory to Algorithms / Cambridge University Press

[book] Andreas C. Müller / 2016 / Introduction to Machine Learning with Python: A Guide for Data Scientists / O'Reilly Media, Inc.

[web] Giuseppe Bonaccorso / 2007 / Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning / https://dl.acm.org/citation.cfm?id=3165154

[journal] Yoshua Bengio / 2004 / No Unbiased Estimator of the Variance of K-Fold Cross-Validation / The Journal of Machine Learning Research 5 : 1089~1105

[journal] Leo Breiman / 1996 / Heuristics of Instability and Stabilization in Model Selection / The Annals of Statistics 24(6) : 2350~2383 / https://projecteuclid.org/download/pdf_1/euclid.aos/1032181158

[journal] Ji-Hyun Kim / 2009 / Estimating Classification Error Rate: Repeated Cross-Validation, Repeated Hold-out and Bootstrap / Computational Statistics & Data Analysis 53 : 3735~3745

[web] Gartner, Analyst / 2017 / Preparing and Architecting for Machine Learning / https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/preparing_and_architecting_for_machine_learning.pdf

[journal] Hawkins, Douglas M. / 2003 / Assessing Model Fit by Cross-Validation / Journal of Chemical Information and Computer Sciences : 579~586

[confproc] Kohavi, Ron / 1995 / A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection / International Joint Conference on Artificial Intelligence : 1137~1143