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: Bayesian Optimization and Data Science
  name:
    name part: Archetti, Francesco.
    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: Candelieri, Antonio.
    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: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 126 p. 52 illus., 39 illus. in color. online resource.
  abstract: This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
  table of contents: 1. Automated Machine Learning and Bayesian Optimization -- 2. From Global Optimization to Optimal Learning -- 3. The Surrogate Model -- 4. The Acquisition Function -- 5. Exotic BO -- 6. Software Resources -- 7. Selected Applications.
  note: by Francesco Archetti, Antonio Candelieri.
  subject:
    topic: Operations research
  subject:
    topic: Management science
  subject:
    topic: Machine learning
  subject:
    topic: Computer software
  subject:
    topic: Statistics
  subject:
    topic: Operations Research, Management Science
  subject:
    topic: Machine Learning
  subject:
    topic: Mathematical Software
  subject:
    topic: Bayesian Inference
  classification: QA402-402.37
  classification: T57.6-57.97
  classification: 519.6
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Optimization
  identifier: 9783030244941
  identifier: https://doi.org/10.1007/978-3-030-24494-1
  record info:
    record creation date: 190925
    record change date: 20191022122233.0
    record identifier: 978-3-030-24494-1