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: Tree-Based Convolutional Neural Networks
    sub title: Principles and Applications
  name:
    name part: Mou, Lili.
    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: Jin, Zhi.
    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: si
    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, 96 p. 32 illus. online resource.
  abstract: This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data. TBCNNsare related to existing convolutional neural networks (CNNs) and recursive neural networks (RNNs), but they combine the merits of both: thanks to their short propagation path, they are as efficient in learning as CNNs; yet they are also as structure-sensitive as RNNs. In this book, readers will also find a comprehensive literature review of related work, detailed descriptions of TBCNNs and their variants, and experiments applied to program analysis and natural language processing tasks. It is also an enjoyable read for all those with a general interest in deep learning.
  table of contents: Introduction -- Preliminaries and Related Work -- General Concepts of Tree-Based Convolutional Neural Networks (TBCNNs) -- TBCNN for Programs' Abstract Syntax Trees (ASTs) -- TBCNN for Constituency Trees in Natural Language Processing -- TBCNN for Dependency Trees in Natural Language Processing -- Concluding Remarks.
  note: by Lili Mou, Zhi Jin.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Data mining
  subject:
    topic: Computational intelligence
  subject:
    topic: Software engineering
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Computational Intelligence
  subject:
    topic: Software Engineering
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computer Science
  identifier: 9789811318702
  identifier: https://doi.org/10.1007/978-981-13-1870-2
  record info:
    record creation date: 181001
    record change date: 20191220130834.0
    record identifier: 978-981-13-1870-2