001438755 000__ 03438cam\a2200553\i\4500
001438755 001__ 1438755
001438755 003__ OCoLC
001438755 005__ 20230309004352.0
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001438755 007__ cr\un\nnnunnun
001438755 008__ 210807s2021\\\\si\a\\\\ob\\\\000\0\eng\d
001438755 019__ $$a1263870828
001438755 020__ $$a9789811614460$$q(electronic bk.)
001438755 020__ $$a9811614466$$q(electronic bk.)
001438755 020__ $$z9789811614453
001438755 020__ $$z9811614458
001438755 0247_ $$a10.1007/978-981-16-1446-0$$2doi
001438755 035__ $$aSP(OCoLC)1263023947
001438755 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dEBLCP$$dGW5XE$$dOCLCO$$dOCLCF$$dUKAHL$$dOCLCQ$$dOCLCO$$dOCLCQ
001438755 049__ $$aISEA
001438755 050_4 $$aQA278$$b.S89 2021
001438755 08204 $$a519.5/35$$223
001438755 1001_ $$aSuzuki, Joe,$$eauthor.
001438755 24510 $$aSparse estimation with math and R :$$b100 exercises for building logic /$$cJoe Suzuki.
001438755 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001438755 264_4 $$c©2021
001438755 300__ $$a1 online resource :$$billustrations (chiefly color)
001438755 336__ $$atext$$btxt$$2rdacontent
001438755 337__ $$acomputer$$bc$$2rdamedia
001438755 338__ $$aonline resource$$bcr$$2rdacarrier
001438755 504__ $$aIncludes bibliographical references.
001438755 5050_ $$aChapter 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.
001438755 506__ $$aAccess limited to authorized users.
001438755 520__ $$aThe 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.
001438755 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 19, 2021).
001438755 650_0 $$aMultivariate analysis.
001438755 650_0 $$aEstimation theory.
001438755 650_0 $$aR (Computer program language)
001438755 650_6 $$aAnalyse multivariée.
001438755 650_6 $$aThéorie de l'estimation.
001438755 650_6 $$aR (Langage de programmation)
001438755 655_0 $$aElectronic books.
001438755 77608 $$iPrint version:$$aSuzuki, Joe.$$tSparse estimation with math and R.$$dSingapore : Springer, [2021]$$z9811614458$$z9789811614453$$w(OCoLC)1240491994
001438755 852__ $$bebk
001438755 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-1446-0$$zOnline Access$$91397441.1
001438755 909CO $$ooai:library.usi.edu:1438755$$pGLOBAL_SET
001438755 980__ $$aBIB
001438755 980__ $$aEBOOK
001438755 982__ $$aEbook
001438755 983__ $$aOnline
001438755 994__ $$a92$$bISE