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: Emerging Paradigms in Machine Learning
  name:
    name part: Ramanna, Sheela.
    role:
      role term: editor.
  name:
    name part: Jain, Lakhmi C.
    role:
      role term: editor.
  name:
    name part: Howlett, Robert J.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXII, 498 p. online resource.
  abstract: This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book. .
  table of contents: From the content: Emerging Paradigms in Machine Learning: An Introduction -- Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization -- Optimised information abstraction in granular Min/Max clustering -- Mining Incomplete Data—A Rough Set Approach -- Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation.
  note: edited by Sheela Ramanna, Lakhmi C Jain, Robert J. Howlett.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Smart Innovation, Systems and Technologies, 13
  identifier: 9783642286995
  identifier: http://dx.doi.org/10.1007/978-3-642-28699-5
  record info:
    record creation date: 120730
    record change date: 20151125221706.0
    record identifier: 978-3-642-28699-5