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:
    title: Machine Learning
    sub title: A Practical Approach on the Statistical Learning Theory
  name:
    name part: F MELLO, RODRIGO.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Antonelli Ponti, Moacir.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 362 p. 190 illus. online resource.
  abstract: This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
  table of contents: Chapter 1 - A Brief Review on Machine Learning -- Chapter 2 - Statistical Learning Theory -- Chapter 3 - Assessing Learning Algorithms -- Chapter 4 - Introduction to Support Vector Machines -- Chapter 5 - In Search for the Optimization Algorithm -- Chapter 6 - A Brief Introduction on Kernels -- .
  note: by RODRIGO F MELLO, Moacir Antonelli Ponti.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mathematical statistics
  subject:
    topic: Computer science-Mathematics
  subject:
    topic: Computer mathematics
  subject:
    topic: Statistics
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Probability and Statistics in Computer Science
  subject:
    topic: Mathematical Applications in Computer Science
  subject:
    topic: Applied Statistics
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319949895
  identifier: https://doi.org/10.1007/978-3-319-94989-5
  record info:
    record creation date: 180801
    record change date: 20191220130834.0
    record identifier: 978-3-319-94989-5