A Gaussian process regression model to predict energy contents of corn for poultry

The present study proposes a Gaussian process regression (GPR) approach to develop a model to predict true metabolizable energy corrected for nitrogen (TMEn) content of corn samples (as model output) for poultry given levels of feed chemical compositions of crude protein, ether extract, crude fiber,...

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Detalles Bibliográficos
Autores principales: Baiz, Abbas Abdullah, Ahmadi, Hamed, Shariatmadari, Farid, Karimi Torshizi, Mohammad Amir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Metabolism and Nutrition
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647822/
https://www.ncbi.nlm.nih.gov/pubmed/33142501
http://dx.doi.org/10.1016/j.psj.2020.07.044

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647822/
https://www.ncbi.nlm.nih.gov/pubmed/33142501
http://dx.doi.org/10.1016/j.psj.2020.07.044

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