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A comparison of dimensionality reduction methods for large ..:
, In:
Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
,
Babjac, Ashley
;
Royalty, Taylor
;
Steen, Andrew D
. - p. 1-7 , 2022
Link:
https://dl.acm.org/doi/10.1145/3535508.3545536
RT T1 <cTitle>Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics</cTitle>: T1 <Title>A comparison of dimensionality reduction methods for large biological data</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3545536&Exemplar=1&LAN=DE A1 Babjac, Ashley A1 Royalty, Taylor A1 Steen, Andrew D A1 Emrich, Scott J PB ACM YR 2022 K1 autoencoders K1 classification K1 dimensionality reduction K1 Computing methodologies K1 Machine learning K1 Machine learning algorithms K1 Feature selection K1 Cross-validation K1 Machine learning approaches K1 Kernel methods K1 Support vector machines K1 Learning latent representations K1 Classification and regression trees K1 Learning paradigms K1 Supervised learning K1 Supervised learning by classification SP 1 OP 7 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3535508.3545536 DO https://dl.acm.org/doi/10.1145/3535508.3545536 SF ELIB - SuUB Bremen
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