Reverse Hypothesis Machine Learning

A Practitioner's Perspective

  • Parag Kulkarni

Part of the Intelligent Systems Reference Library book series (ISRL, volume 128)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Building Foundation: Decoding Knowledge Acquisition

    1. Front Matter
      Pages 1-1
    2. Parag Kulkarni
      Pages 3-22
    3. Parag Kulkarni
      Pages 49-58
  3. Learnability Route: Reverse Hypothesis Machines

    1. Front Matter
      Pages 85-85
    2. Parag Kulkarni
      Pages 87-118
    3. Parag Kulkarni
      Pages 133-134
  4. Back Matter
    Pages 135-138

About this book


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.


Intelligent Systems Knowledge Information Systems Machine Learning Creative Machines Creative Machine Learning

Authors and affiliations

  • Parag Kulkarni
    • 1
  1. 1.iknowlation Research Labs Pvt Ltd.PuneIndia

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-55311-5
  • Online ISBN 978-3-319-55312-2
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site
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