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: Rule Extraction from Support Vector Machines
  name:
    name part: Diederich, Joachim.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 262 p. 55 illus. online resource.
  abstract: Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.
  table of contents: Rule Extraction from Support Vector Machines: An Introduction -- Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring -- Algorithms and Techniques -- Rule Extraction for Transfer Learning -- Rule Extraction from Linear Support Vector Machines via Mathematical Programming -- Rule Extraction Based on Support and Prototype Vectors -- SVMT-Rule: Association Rule Mining Over SVM Classification Trees -- Prototype Rules from SVM -- Applications -- Prediction of First-Day Returns of Initial Public Offering in the US Stock Market Using Rule Extraction from Support Vector Machines -- Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction -- Rule Extraction from SVM for Protein Structure Prediction.
  note: edited by Joachim Diederich.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Engineering
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: TA329-348
  classification: TA640-643
  classification: 519
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 80
  identifier: 9783540753902
  identifier: http://dx.doi.org/10.1007/978-3-540-75390-2
  record info:
    record creation date: 100301
    record change date: 20151204173610.0
    record identifier: 978-3-540-75390-2