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: The
    title: Elements of Statistical Learning
    sub title: Data Mining, Inference, and Prediction
  name:
    name part: Hastie, Trevor.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Tibshirani, Robert.
    role:
      role term: author.
  name:
    name part: Friedman, Jerome.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2009
    edition: Second Edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXII, 745 p. 640 illus., 604 illus. in color. online resource.
  abstract: During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: 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. Friedman is the co-inventor of ma…
  table of contents: Overview of Supervised Learning -- Linear Methods for Regression -- Linear Methods for Classification -- Basis Expansions and Regularization -- Kernel Smoothing Methods -- Model Assessment and Selection -- Model Inference and Averaging -- Additive Models, Trees, and Related Methods -- Boosting and Additive Trees -- Neural Networks -- Support Vector Machines and Flexible Discriminants -- Prototype Methods and Nearest-Neighbors -- Unsupervised Learning -- Random Forests -- Ensemble Learning -- Undirected Graphical Models -- High-Dimensional Problems: p ? N.
  note: by Trevor Hastie, Robert Tibshirani, Jerome Friedman.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Bioinformatics
  subject:
    topic: Computational biology
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Computational Biology/Bioinformatics
  subject:
    topic: Computer Appl. in Life Sciences
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Series in Statistics
  identifier: 9780387848587
  identifier: http://dx.doi.org/10.1007/978-0-387-84858-7
  record info:
    record creation date: 100301
    record change date: 20170216141520.0
    record identifier: 978-0-387-84858-7