東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Sparse estimation with math and R = ...

Suzuki, Joe.

Linked to FindBook

Google Book

Amazon

博客來

Sparse estimation with math and R = 100 exercises for building logic /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sparse estimation with math and R/ by Joe Suzuki.
Reminder of title:	100 exercises for building logic /
Author:	Suzuki, Joe.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	x, 234 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis.
Contained By:	Springer Nature eBook
Subject:	Multivariate analysis. -
Online resource:	https://doi.org/10.1007/978-981-16-1446-0
ISBN:	9789811614460

Sparse estimation with math and R = 100 exercises for building logic /
Suzuki, Joe.

Sparse estimation with math and R100 exercises for building logic /[electronic resource] :by Joe Suzuki. - Singapore :Springer Singapore :2021. - x, 234 p. :ill., digital ;24 cm.

Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers' insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each) Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.

ISBN: 9789811614460

Standard No.: 10.1007/978-981-16-1446-0doiSubjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278 / .S89 2021

Dewey Class. No.: 519.535

Sparse estimation with math and R = 100 exercises for building logic /
LDR:02439nmm a2200325 a 4500 001 2242093
003 DE-He213
005 20210804075001.0
006 m d
007 cr nn 008maaau
008 211207s2021 si s 0 eng d
020 $a 9789811614460 $q (electronic bk.)
020 $a 9789811614453 $q (paper)
024 7 $a 10.1007/978-981-16-1446-0 $2 doi
035 $a 978-981-16-1446-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278 $b .S89 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 519.535 $2 23
090 $a QA278 $b .S968 2021
100 1 $a Suzuki, Joe. $3 2165769
245 1 0 $a Sparse estimation with math and R $h [electronic resource] : $b 100 exercises for building logic / $c by Joe Suzuki.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a x, 234 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis.
520 $a The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers' insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each) Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.
650 0 $a Multivariate analysis. $3 517467
650 0 $a Estimation theory. $3 565962
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Statistics, general. $3 896933
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-16-1446-0
950 $a Computer Science (SpringerNature-11645)