An Introduction to Statistical Learning: With Applications in R
Gareth James/ Computers & Internet
An Introduction to Statistical Learning: With Applications in R Gareth James texte en entier pdf - An Introduction to Statistical Learning: With Applications in R par Gareth James ont été vendues pour EUR 47,20 chaque exemplaire. Le livre publié par Springer-Verlag New York Inc.. Il contient 426 pages et classé dans le genre Computers & Internet. Ce livre a une bonne réponse du lecteur, il a la cote 4.5 des lecteurs 865. Inscrivez-vous maintenant pour accéder à des milliers de livres disponibles pour téléchargement gratuit. L'inscription était gratuite.
Détails de An Introduction to Statistical Learning: With Applications in R
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Titre du livre : An Introduction to Statistical Learning: With Applications in R
Auteur : Gareth James
ISBN-10 : 1461471370
Date de sortie : 2013-06-25
Catégorie : Computers & Internet
Nom de fichier : an-introduction-to-statistical-learning-with-applications-in-r.pdf
Taille du fichier : 22.23 (La vitesse du serveur actuel est 21.18 Mbps
An Introduction to Statistical Learning: With Applications in R Gareth James texte en entier pdf - An Introduction to Statistical Learning This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.Rang parmi les ventes Amazon: #2332 dans LivresPublié le: 2013-06-25Langue d'origine: AnglaisNombre d'articles: 1Dimensions: 9.25" h x .85" l x 6.25" L, 2.00 livres Reliure: Relié426 pagesPrésentation de l'éditeurAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.Biographie de l'auteurGareth James is a professor of statistics at University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area. Daniela Wittenis an assistant professor of biostatistics at University of Washington. Her research focuses largely on high-dimensional statistical machine learning. She has contributed to the translation of statistical learning techniques to the field of genomics, through collaborations and as a member of the Institute of Medicine committee that led to the report Evolution of Translational Omics.Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.
Catégories : Computers & Internet
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