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: Optimization for machine learning
  name:
    name part: Sra, Suvrit
    name part: 1976-
  name:
    name part: Nowozin, Sebastian
    name part: 1980-
  name:
    name part: Wright, Stephen J.
    name part: 1960-
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    date issued: 2012
    copyright date: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (ix, 494 pages) : illustrations.
  abstract: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
  note: edited by Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright.
  subject:
    topic: Machine learning
    topic: Mathematical models
  subject:
    topic: Mathematical optimization
  subject:
    topic: COMPUTER SCIENCE/Machine Learning & Neural Networks
  subject:
    topic: COMPUTER SCIENCE/Artificial Intelligence
  classification: Q325.5 .O65 2012eb
  classification: 006.3/1
  identifier: 9780262298773
  identifier: 0262298775
  identifier: 1283302845
  identifier: 9781283302845
  identifier: 9780262016469
  identifier: 026201646X
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OCoLC-P
    record creation date: 111024
    record change date: 20221121162627.9
    record identifier: in00004602306
    language of cataloging:
      language term: eng