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: Reverse Hypothesis Machine Learning
    sub title: A Practitioner's Perspective
  name:
    name part: Kulkarni, Parag.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 138 p. 61 illus., 9 illus. in color. online resource.
  abstract: This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.
  table of contents: Pattern Apart -- Understanding Machine Learning Opportunities -- Systemic Machine Learning -- Reinforcement and Deep Reinforcement Machine Learning -- Creative Machine Learning -- Co-operative and Collective learning for Creative Machine Learning -- Building Creative Machines with Optimal Machine Learning and Creative Machine Learning Applications -- Conclusion – Learning Continues.
  note: by Parag Kulkarni.
  subject:
    topic: Engineering
  subject:
    topic: Knowledge management
  subject:
    topic: Management
  subject:
    topic: Industrial management
  subject:
    topic: Computational intelligence
  subject:
    topic: Machinery
  subject:
    topic: Electronics
  subject:
    topic: Microelectronics
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Knowledge Management
  subject:
    topic: Machinery and Machine Elements
  subject:
    topic: Innovation/Technology Management
  subject:
    topic: Electronics and Microelectronics, Instrumentation
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Intelligent Systems Reference Library, 128
  identifier: 9783319553122
  identifier: http://dx.doi.org/10.1007/978-3-319-55312-2
  record info:
    record creation date: 170330
    record change date: 20170330092246.0
    record identifier: 978-3-319-55312-2