001431265 000__ 04423cam\a2200601\i\4500
001431265 001__ 1431265
001431265 003__ OCoLC
001431265 005__ 20230308003228.0
001431265 006__ m\\\\\o\\d\\\\\\\\
001431265 007__ cr\un\nnnunnun
001431265 008__ 220916s2022\\\\sz\a\\\\ob\\\\000\0\eng\d
001431265 019__ $$a1294143848$$a1294343798$$a1294428573$$a1294453003$$a1296667062$$a1298437702$$a1303412245$$a1330572170
001431265 020__ $$a9783030890100$$q(ebook)
001431265 020__ $$a3030890104
001431265 020__ $$z9783030890094
001431265 020__ $$z3030890090
001431265 0247_ $$a10.1007/978-3-030-89010-0$$2doi
001431265 035__ $$aSP(OCoLC)1294307405
001431265 040__ $$aNLM$$beng$$erda$$cNLM$$dYDX$$dEBLCP$$dDKU$$dOCLCF$$dGW5XE$$dOCLCQ$$dYWS$$dJF0
001431265 042__ $$apcc
001431265 049__ $$aISEA
001431265 050_4 $$aQK981.5
001431265 08204 $$a581.3/50727$$223
001431265 1001_ $$aMontesinos López, Osval Antonio,$$eauthor.
001431265 24510 $$aMultivariate statistical machine learning methods for genomic prediction /$$cOsval Antonio Montesinos López, Abelardo Montesinos López, Jose Crossa.
001431265 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2022]
001431265 300__ $$a1 online resource
001431265 336__ $$atext$$btxt$$2rdacontent
001431265 337__ $$acomputer$$bc$$2rdamedia
001431265 338__ $$aonline resource$$bcr$$2rdacarrier
001431265 347__ $$atext file$$bPDF$$2rda
001431265 504__ $$aIncludes bibliographical references and index.
001431265 5050_ $$aPreface -- Chapter 1 -- General elements of genomic selection and statistical learning -- Chapter. 2 -- Preprocessing tools for data preparation -- Chapter. 3 -- Elements for building supervised statistical machine learning models -- Chapter. 4 -- Overfitting, model tuning and evaluation of prediction performance -- Chapter. 5 -- Linear Mixed Models -- Chapter. 6 -- Bayesian Genomic Linear Regression -- Chapter. 7 -- Bayesian and classical prediction models for categorical and count data -- Chapter. 8 -- Reproducing Kernel Hilbert Spaces Regression and Classification Methods -- Chapter. 9 -- Support vector machines and support vector regression -- Chapter. 10 -- Fundamentals of artificial neural networks and deep learning -- Chapter. 11 -- Artificial neural networks and deep learning for genomic prediction of continuous outcomes -- Chapter. 12 -- Artificial neural networks and deep learning for genomic prediction of binary, ordinal and mixed outcomes -- Chapter. 13 -- Convolutional neural networks -- Chapter. 14 -- Functional regression -- Chapter. 15 -- Random forest for genomic prediction.
001431265 5060_ $$aOpen access.$$5GW5XE
001431265 5203_ $$aThis open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
001431265 588__ $$aDescription based on online resource; title from PDF title page (viewed December 15, 2022).
001431265 650_0 $$aPlant genetics$$xStatistical methods.
001431265 650_0 $$aMultivariate analysis$$xData processing.
001431265 650_0 $$aMachine learning.
001431265 650_6 $$aGénétique végétale$$xMéthodes statistiques.
001431265 650_6 $$aAnalyse multivariée$$xInformatique.
001431265 650_6 $$aApprentissage automatique.
001431265 655_0 $$aElectronic books.
001431265 7001_ $$aMontesinos López, Abelardo,$$eauthor.
001431265 7001_ $$aCrossa, José,$$eauthor.
001431265 7730_ $$tSpringer Nature eBook
001431265 77608 $$iPrint version: $$z3030890090$$z9783030890094$$w(OCoLC)1267585593
001431265 852__ $$bebk
001431265 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-3-030-89010-0$$zOnline Access$$91397441.2
001431265 909CO $$ooai:library.usi.edu:1431265$$pGLOBAL_SET
001431265 980__ $$aBIB
001431265 980__ $$aEBOOK
001431265 982__ $$aEbook
001431265 983__ $$aOnline
001431265 994__ $$a92$$bISE