Overview
- Presents a practitioner’s perspective on knowledge innovation and machine learning
- Discusses different aspects of knowledge innovation applied to systemic machine learning paradigms
- Includes case studies on building creative machines including learning components for various real life problems
- Includes supplementary material: sn.pub/extras
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 128)
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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.
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Table of contents (8 chapters)
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Front Matter
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Building Foundation: Decoding Knowledge Acquisition
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Front Matter
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Learnability Route: Reverse Hypothesis Machines
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Reverse Hypothesis Machine Learning
Book Subtitle: A Practitioner's Perspective
Authors: Parag Kulkarni
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-55312-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-55311-5Published: 06 April 2017
Softcover ISBN: 978-3-319-85626-1Published: 25 July 2018
eBook ISBN: 978-3-319-55312-2Published: 30 March 2017
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVI, 138
Number of Illustrations: 52 b/w illustrations, 9 illustrations in colour
Topics: Computational Intelligence, Knowledge Management, Machinery and Machine Elements, Innovation/Technology Management, Electronics and Microelectronics, Instrumentation