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1
Reporting Statistical Validity and Model Complexity in Mach..:
, In:
Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering
,
Olorisade, Babatunde Kazeem
;
Brereton, Pearl
;
Andras, Peter
- p. 128-133 , 2017
Link:
https://dl.acm.org/doi/10.1145/3084226.3084283
RT T1 <cTitle>Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering</cTitle>: T1 <Title>Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3084283&Exemplar=1&LAN=DE A1 Olorisade, Babatunde Kazeem A1 Brereton, Pearl A1 Andras, Peter PB ACM YR 2017 K1 CS automation K1 Model complexity K1 model selection K1 statistical validity K1 systematic reviews K1 text mining K1 General and reference K1 Cross-computing tools and techniques K1 Experimentation K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Supervised learning K1 Supervised learning by classification K1 Machine learning approaches K1 Kernel methods K1 Support vector machines K1 Empirical studies SP 128 OP 133 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3084226.3084283 DO https://dl.acm.org/doi/10.1145/3084226.3084283 SF ELIB - SuUB Bremen
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