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: Feature Learning and Understanding
    sub title: Algorithms and Applications
  name:
    name part: Zhao, Haitao.
    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: Lai, Zhihui.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Leung, Henry.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Zhang, Xianyi.
    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
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2020
    edition: 1st ed. 2020.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XIV, 291 pages 126 illustrations, 109 illustrations in color.)
  abstract: This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.
  table of contents: Chapter1. A Gentle Introduction to Feature Learning -- Chapter2. Latent Semantic Feature Learning -- Chapter3. Principal Component Analysis -- Chapter4. Local-Geometrical-Structure-based Feature Learning -- Chapter5. Linear Discriminant Analysis -- Chapter6. Kernel-based nonlinear feature learning -- Chapter7. Sparse feature learning -- Chapter8. Low rank feature learning -- Chapter9. Tensor-based Feature Learning -- Chapter10. Neural-network-based Feature Learning: Autoencoder -- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network -- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network.
  note: by Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang.
  subject:
    topic: Sociophysics
  subject:
    topic: Econophysics
  subject:
    topic: Machine learning
  subject:
    topic: Computational intelligence
  subject:
    topic: Pattern recognition
  subject:
    topic: Signal processing
  subject:
    topic: Image processing
  subject:
    topic: Speech processing systems
  subject:
    topic: Optical data processing
  subject:
    topic: Data-driven Science, Modeling and Theory Building
  subject:
    topic: Machine Learning
  subject:
    topic: Computational Intelligence
  subject:
    topic: Pattern Recognition
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Image Processing and Computer Vision
  classification: QC1-999
  classification: 621
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Information Fusion and Data Science
  identifier: 9783030407940
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 200403
    record change date: 20230330185234.1
    record identifier: in00004381163