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

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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Bibliographic Details
Main Authors:	Abbas Abdullah Baiz, Hamed Ahmadi, Farid Shariatmadari, Mohammad Amir Karimi Torshizi
Format:	Article
Language:	English
Published:	Elsevier 2020-11-01
Series:	Poultry Science
Subjects:	corn metabolizable energy Gaussian process regression prediction model
Online Access:	http://www.sciencedirect.com/science/article/pii/S0032579120305265

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