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: Multiview Machine Learning
  name:
    name part: Sun, Shiliang.
    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: Mao, Liang.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Dong, Ziang.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Wu, Lidan.
    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: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 149 p. 10 illus., 7 illus. in color. online resource.
  abstract: This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.
  table of contents: Chapter 1. Introduction -- Chapter 2. Multiview Semi-supervised Learning -- Chapter 3. Multiview Subspace Learning -- Chapter 4. Multiview Supervised Learning -- Chapter 5. Multiview Clustering -- Chapter 6. Multiview Active Learning -- Chapter 7. Multiview Transfer Learning and Multitask Learning -- Chapter 8. Multiview Deep Learning -- Chapter 9. View Construction.
  note: by Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Pattern recognition
  subject:
    topic: Optical data processing
  subject:
    topic: Data mining
  subject:
    topic: Big data
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Pattern Recognition
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Big Data
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9789811330292
  identifier: https://doi.org/10.1007/978-981-13-3029-2
  record info:
    record creation date: 190107
    record change date: 20191025201724.0
    record identifier: 978-981-13-3029-2