Título:
Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis
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Autor:
Fariello, Maria Ines
Armstrong, Eileen
Fernández, Alicia
Armstrong, Eileen
Fernández, Alicia
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
Parametric
Non parametric
Genomic
Selection
Prediction
Fusion
Non parametric
Genomic
Selection
Prediction
Fusion
Descriptores:
Procesamiento de Señales
Año de publicación:
2015
Contenido:
Resumen:
Genomic prediction is a still growing field, as good predictions can have important economic impact in both, agronomics and health. In this article, we make a brief review and a comprehensive analysis of classical predictors used in the area. We propose a strategy to choose and ensemble of methods and to combine their results, to take advantage of the complementarity that some predictors have.
Descripción:
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Editorial:
Springer International Publishing
EN:
20th Iberoamerican Congress, CIARP 2015, Montevideo, Uruguay, 9-12 nov, 2015
Financiadores:
Citación:
Fariello, M.I., Amstrong, E., Fernandez, A. "Genetic prediction in bovine meat production: is worth integrating bayesian and machine learning approaches? A comprenhensive analysis" Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science, vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_2
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imágenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| FAF15.pdf | — | 227.07 KB | Adobe PDF |