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Results from parameter optimisation for HC-RNN with various..:
Elise Lunde Gjelsvik
;
Kristin Tøndel
doi:10.1371/journal.pone.0295251.g004. , 2023
Link:
https://doi.org/10.1371/journal.pone.0295251.g004
RT Journal T1
Results from parameter optimisation for HC-RNN with various activation functions and learning rates
UL https://suche.suub.uni-bremen.de/peid=base-ftdeakinunifig:oai:figshare.com:article_24766234&Exemplar=1&LAN=DE A1 Elise Lunde Gjelsvik A1 Kristin Tøndel YR 2023 K1 Plant Biology K1 Environmental Sciences not elsewhere classified K1 Biological Sciences not elsewhere classified K1 Mathematical Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 fourier transform infrared K1 average molecular weight K1 contain three clusters K1 various local models K1 spectroscopic data consisting K1 simulated data set K1 local modelling approach K1 svr outperformed hc K1 local model within K1 deep learning models K1 ir data set K1 local modelling K1 deep learning K1 complex models K1 smaller clusters K1 linear data K1 data dominate K1 output space K1 locally linear K1 large inhomogeneity K1 independent variables K1 highly non K1 highlighting differences K1 feature importance K1 different regions K1 different non K1 abrupt non JF doi:10.1371/journal.pone.0295251.g004 LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0295251.g004 DO https://doi.org/10.1371/journal.pone.0295251.g004 SF ELIB - SuUB Bremen
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