Compartir
Multivariate Statistical Machine Learning Methods for Genomic Prediction
Osval Antonio Montesinos López
(Autor)
·
Abelardo Montesinos López
(Autor)
·
José Crossa
(Autor)
·
Springer
· Tapa Dura
Multivariate Statistical Machine Learning Methods for Genomic Prediction - Montesinos López, Osval Antonio ; Montesinos López, Abelardo ; Crossa, José
Sin Stock
Te enviaremos un correo cuando el libro vuelva a estar disponible
Reseña del libro "Multivariate Statistical Machine Learning Methods for Genomic Prediction"
Preface.- Chapter 1.- General elements of genomic selection and statistical learning.- Chapter. 2.- Preprocessing tools for data preparation.- Chapter. 3.- Elements for building supervised statistical machine learning models.- Chapter. 4.- Overfitting, model tuning and evaluation of prediction performance.- Chapter. 5.- Linear Mixed Models.- Chapter. 6.- Bayesian Genomic Linear Regression.- Chapter. 7.- Bayesian and classical prediction models for categorical and count data.- Chapter. 8.- Reproducing Kernel Hilbert Spaces Regression and Classification Methods.- Chapter. 9.- Support vector machines and support vector regression.- Chapter. 10.- Fundamentals of artificial neural networks and deep learning.- Chapter. 11.- Artificial neural networks and deep learning for genomic prediction of continuous outcomes.- Chapter. 12.- Artificial neural networks and deep learning for genomic prediction of binary, ordinal and mixed outcomes.- Chapter. 13.- Convolutional neural networks.- Chapter. 14.- Functional regression.- Chapter. 15.- Random forest for genomic prediction.
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
Todos los libros de nuestro catálogo son Originales.
El libro está escrito en Inglés.
La encuadernación de esta edición es Tapa Dura.
✓ Producto agregado correctamente al carro, Ir a Pagar.