rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: An
    title: introduction to statistical learning
    sub title: with applications in R
  title info:
    title: Statistical learning
  name:
    name part: James, Gareth (Gareth Michael)
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Witten, Daniela
    role:
      role term: author.
  name:
    name part: Hastie, Trevor
    role:
      role term: author.
  name:
    name part: Tibshirani, Robert
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Problems, exercises, etc.
  origin info:
    place:
      place term: nyu
    date issued: 2014
    copyright date: 2014
    edition: Corrected edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xiv, 426 pages : illustrations (some color) ; 24 cm.
  abstract: "An 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. Provides tools for Statistical Learning that are essential for practitioners in science, industry and other fields. Analyses and methods are presented in R. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Extensive use of color graphics assist the reader"--Publisher description.
  table of contents: Introduction -- Statistical learning -- Linear regression -- Classification -- Resampling methods -- Linear model selection and regularization -- Moving beyond linearity -- Tree-based methods -- Support vector machines -- Unsupervised learning.
  note: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
  note: Includes bibliographical references and index.
  subject:
    topic: Mathematical statistics
    genre: Problems, exercises, etc
  subject:
    topic: Mathematical models
    genre: Problems, exercises, etc
  subject:
    topic: R (Computer program language)
  subject:
    topic: Statistics
  subject:
    topic: Statistique mathématique
  subject:
    topic: Modèles mathématiques
  subject:
    topic: Statistique mathématique
    topic: Problèmes et exercices
  subject:
    topic: Modèles mathématiques
    topic: Problèmes et exercices
  subject:
    topic: Mathematical models
  subject:
    topic: Mathematical statistics
  subject:
    topic: R (Computer program language)
  subject:
    topic: Statistics
  subject:
    topic: Statistik
  subject:
    topic: Maschinelles Lernen
  classification: QA276 .I585 2014
  classification: QA276.J36 I58 2014
  classification: 519.5
  related item:
    title info:
      title: Springer texts in statistics
  identifier: 9781461471370 (acid-free paper)
  identifier: 1461471370 (acid-free paper)
  identifier: 9781461471387 (eBook)
  identifier: 1461471389 (eBook)
  identifier: 2013936251
  record info:
    record content source: BTCTA
    record creation date: 130224
    record change date: 20151206125018.0
    record identifier: in00003499989
    language of cataloging:
      language term: eng