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: Informational Complexity of Learning
    sub title: Perspectives on Neural Networks and Generative Grammar
  name:
    name part: Niyogi, Partha.
    role:
      role term: creator
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    date issued: 1998
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (248 pages)
  abstract: Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework. The first concerns the problem of learning functional mappings using neural networks, followed by learning natural language grammars in the principles and parameters tradition of Chomsky. These two learning problems are seemingly very different. Neural networks are real-valued, infinite-dimensional, continuous mappings. On the other hand, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. Furthermore the research communities that work in the two areas almost never overlap. The book's objective is to bridge this gap. It uses the formal techniques developed in statistical learning theory and theoretical computer science over the last decade to analyze both kinds of learning problems. By asking the same question - how much information does it take to learn? - of both problems, it highlights their similarities and differences. Specific results include model selection in neural networks, active learning, language learning and evolutionary models of language change. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the interaction of computer science and cognitive science should enjoy the book. Researchers in artificial intelligence, neural networks, linguistics, theoretical computer science, and statistics will find it particularly relevant.
  note: by Partha Niyogi.
  note: Electronic resource.
  subject:
    topic: Computational linguistics
  subject:
    topic: Language acquisition
  subject:
    topic: Linguistic change
  subject:
    topic: Machine learning
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Computational linguistics
  subject:
    topic: Language acquisition
  subject:
    topic: Linguistic change
  subject:
    topic: Machine learning
  subject:
    topic: Neural networks (Computer science)
  classification: P98
  classification: 006.3
  identifier: 9781461554592 (electronic bk.)
  identifier: 1461554594 (electronic bk.)
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: I9W
    record creation date: 121227
    record change date: 20260422161207.0
    record identifier: in00003543249
    language of cataloging:
      language term: eng