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: Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
  name:
    name part: Pham, Thuy T.
    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: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    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: XV, 107 p. 35 illus., 32 illus. in color. online resource.
  abstract: This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.
  table of contents: Introduction -- Background -- Algorithms -- Point Anomaly Detection: Application to Freezing of Gait Monitoring -- Collective Anomaly Detection: Application to Respiratory Artefact Removals -- Spike Sorting: Application to Motor Unit Action Potential Discrimination -- Conclusion .
  note: by Thuy T. Pham.
  subject:
    topic: Biomedical engineering
  subject:
    topic: Data mining
  subject:
    topic: Computational intelligence
  subject:
    topic: Bioinformatics
  subject:
    topic: Biomedical Engineering and Bioengineering
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Computational Intelligence
  subject:
    topic: Bioinformatics
  classification: R856-857
  classification: HC79.E5
  classification: GE220
  classification: 610.28
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Theses, Recognizing Outstanding Ph.D. Research
  identifier: 9783319986753
  identifier: https://doi.org/10.1007/978-3-319-98675-3
  record info:
    record creation date: 180823
    record change date: 20191022172044.0
    record identifier: 978-3-319-98675-3