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: Machine Learning Paradigms
    sub title: Applications in Recommender Systems
  name:
    name part: Lampropoulos, Aristomenis S.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Tsihrintzis, George A.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 125 p. 32 illus., 6 illus. in color. online resource.
  abstract: This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely. .
  table of contents: Introduction -- Review of Previous Work Related to Recommender Systems -- The Learning Problem.-Content Description of Multimedia Data -- Similarity Measures for Recommendations based on Objective Feature Subset Selection -- Cascade Recommendation Methods -- Evaluation of Cascade Recommendation Methods -- Conclusions and Future Work.
  note: by Aristomenis S. Lampropoulos, George A. Tsihrintzis.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer graphics
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Computer Imaging, Vision, Pattern Recognition and Graphics
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Intelligent Systems Reference Library, 92
  identifier: 9783319191355
  identifier: http://dx.doi.org/10.1007/978-3-319-19135-5
  record info:
    record creation date: 150613
    record change date: 20151030051302.0
    record identifier: 978-3-319-19135-5