東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Multivariate statistical machine lea...

Montesinos Lopez, Osval Antonio.

FindBook

Google Book

Amazon

博客來

Multivariate statistical machine learning methods for genomic prediction

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate statistical machine learning methods for genomic prediction/ by Osval Antonio Montesinos Lopez, Abelardo Montesinos Lopez, Jose Crossa ; foreword by Fred van Euwijk.
作者:	Montesinos Lopez, Osval Antonio.
其他作者:	Montesinos Lopez, Abelardo.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xxiv, 691 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Plant genetics - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-030-89010-0
ISBN:	9783030890100

Multivariate statistical machine learning methods for genomic prediction
Montesinos Lopez, Osval Antonio.

Multivariate statistical machine learning methods for genomic prediction[electronic resource] /by Osval Antonio Montesinos Lopez, Abelardo Montesinos Lopez, Jose Crossa ; foreword by Fred van Euwijk. - Cham :Springer International Publishing :2022. - xxiv, 691 p. :ill. (some col.), digital ;24 cm.

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.

Open access.

This book is open access under a CC BY 4.0 license This 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.

ISBN: 9783030890100

Standard No.: 10.1007/978-3-030-89010-0doiSubjects--Topical Terms:

3591599
Plant genetics
--Statistical methods.

LC Class. No.: QK981.5 / .M65 2022

Dewey Class. No.: 581.350727

Multivariate statistical machine learning methods for genomic prediction
LDR:03414nmm a2200337 a 4500 001 2296707
003 DE-He213
005 20220113231824.0
006 m d
007 cr nn 008maaau
008 230324s2022 sz s 0 eng d
020 $a 9783030890100 $q (electronic bk.)
020 $a 9783030890094 $q (paper)
024 7 $a 10.1007/978-3-030-89010-0 $2 doi
035 $a 978-3-030-89010-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QK981.5 $b .M65 2022
072 7 $a TVB $2 bicssc
072 7 $a TEC003000 $2 bisacsh
072 7 $a TVB $2 thema
082 0 4 $a 581.350727 $2 23
090 $a QK981.5 $b .M779 2022
100 1 $a Montesinos Lopez, Osval Antonio. $3 3591595
245 1 0 $a Multivariate statistical machine learning methods for genomic prediction $h [electronic resource] / $c by Osval Antonio Montesinos Lopez, Abelardo Montesinos Lopez, Jose Crossa ; foreword by Fred van Euwijk.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xxiv, 691 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
506 $a Open access.
520 $a This book is open access under a CC BY 4.0 license This 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.
650 0 $a Plant genetics $x Statistical methods. $3 3591599
650 0 $a Multivariate analysis $x Data processing. $3 677684
650 0 $a Machine learning. $3 533906
650 1 4 $a Agriculture. $3 518588
650 2 4 $a Bioinformatics. $3 553671
650 2 4 $a Plant Genetics. $3 3531292
650 2 4 $a Agricultural Genetics. $3 3591600
650 2 4 $a Statistics in Life Sciences, Medicine, Health Sciences. $3 3538795
700 1 $a Montesinos Lopez, Abelardo. $3 3591596
700 1 $a Crossa, Jose. $3 3591597
700 1 $a Euwijk, Fred van, $e foreworder. $3 3591598
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-89010-0
950 $a Biomedical and Life Sciences (SpringerNature-11642)