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: Data Complexity in Pattern Recognition
  name:
    name part: Basu, Mitra.
    role:
      role term: editor.
  name:
    name part: Ho, Tin Kam.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxk
    date issued: 2006
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 300 p. online resource.
  abstract: Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach. This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and asks: • What is missing from current classification techniques? • When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task? • How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data? Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.
  table of contents: Theory and Methodology -- Measures of Geometrical Complexity in Classification Problems -- Object Representation, Sample Size, and Data Set Complexity -- Measures of Data and Classifier Complexity and the Training Sample Size -- Linear Separability in Descent Procedures for Linear Classifiers -- Data Complexity, Margin-Based Learning, and Popper’s Philosophy of Inductive Learning -- Data Complexity and Evolutionary Learning -- Classifier Domains of Competence in Data Complexity Space -- Data Complexity Issues in Grammatical Inference -- Applications -- Simple Statistics for Complex Feature Spaces -- Polynomial Time Complexity Graph Distance Computation for Web Content Mining -- Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles -- Complexity of Magnetic Resonance Spectrum Classification -- Data Complexity in Tropical Cyclone Positioning and Classification -- Human-Computer Interaction for Complex Pattern Recognition Problems -- Complex Image Recognition and Web Security.
  note: edited by Mitra Basu, Tin Kam Ho.
  subject:
    topic: Computer science
  subject:
    topic: Algorithms
  subject:
    topic: Artificial intelligence
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: Pattern Recognition
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Algorithm Analysis and Problem Complexity
  classification: Q337.5
  classification: TK7882.P3
  classification: 006.4
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advanced Information and Knowledge Processing
  identifier: 9781846281723
  identifier: http://dx.doi.org/10.1007/978-1-84628-172-3
  record info:
    record creation date: 100301
    record change date: 20151204145953.0
    record identifier: 978-1-84628-172-3