Sparse Estimation with Math and R

Sparse Estimation with Math and R

Author: Joe Suzuki

Publisher: Springer

ISBN: 9811614458

Category: Computers

Page: 234

View: 516

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. This book is one of a series of textbooks in machine learning by the same author. Other titles are: - Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679) - Statistical Learning with Math and Python (https://www.springer.com/gp/book/9789811578762) - Sparse Estimation with Math and Python
Sparse Estimation with Math and R
Language: en
Pages: 234
Authors: Joe Suzuki
Categories: Computers
Type: BOOK - Published: 2021-08-05 - Publisher: Springer

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
Sparse Estimation with Math and R
Language: en
Pages: 234
Authors: Joe Suzuki
Categories: Computers
Type: BOOK - Published: 2021-08-04 - Publisher: Springer Nature

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
Sparse Estimation with Math and Python
Language: en
Pages: 200
Authors: Joe Suzuki
Categories: Computers
Type: BOOK - Published: 2021-08-23 - Publisher: Springer

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 Python programs.
Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems
Language: en
Pages: 254
Authors: Vladimir Koltchinskii
Categories: Mathematics
Type: BOOK - Published: 2011-07-29 - Publisher: Springer

The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, there have been new developments in this area motivated by the study of new classes
Intelligent Mathematics II: Applied Mathematics and Approximation Theory
Language: en
Pages: 502
Authors: George A. Anastassiou, Oktay Duman
Categories: Computers
Type: BOOK - Published: 2016-03-21 - Publisher: Springer

This special volume is a collection of outstanding more applied articles presented in AMAT 2015 held in Ankara, May 28-31, 2015, at TOBB Economics and Technology University. The collection is suitable for Applied and Computational Mathematics and Engineering practitioners, also for related graduate students and researchers. Furthermore it will be