Studies in Computational Intelligence

Empirical Approach to Machine Learning

Authors: Angelov, Plamen Parvanov, Gu, Xiaowei

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  • New efficient methods for pattern recognition and machine learning in data-rich environments
  • Focuses on automated methods, which can be easily adapted to various applications
  • Covers techniques with high level of autonomy, capable to deal with complex, heterogeneous data streams
  • Discusses key case studies and industrial applications
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eBook 118,99 €
price for Spain (gross)
  • ISBN 978-3-030-02384-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 155,99 €
price for Spain (gross)
  • ISBN 978-3-030-02383-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code.
Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA, and Member of the National Academy of Engineering, USA: “The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing.”

Paul J. Werbos, Inventor of the back-propagation method, USA: “I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain.” 
Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: “This new book will set up a milestone for the modern intelligent systems.”
Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: “Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.”

Table of contents (15 chapters)

  • Introduction

    Angelov, Plamen P. (et al.)

    Pages 1-14

    Preview Buy Chapter 30,19 €
  • Brief Introduction to Statistical Machine Learning

    Angelov, Plamen P. (et al.)

    Pages 17-67

    Preview Buy Chapter 30,19 €
  • Brief Introduction to Computational Intelligence

    Angelov, Plamen P. (et al.)

    Pages 69-99

    Preview Buy Chapter 30,19 €
  • Empirical Approach—Introduction

    Angelov, Plamen P. (et al.)

    Pages 103-133

    Preview Buy Chapter 30,19 €
  • Empirical Fuzzy Sets and Systems

    Angelov, Plamen P. (et al.)

    Pages 135-155

    Preview Buy Chapter 30,19 €

Buy this book

eBook 118,99 €
price for Spain (gross)
  • ISBN 978-3-030-02384-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 155,99 €
price for Spain (gross)
  • ISBN 978-3-030-02383-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Empirical Approach to Machine Learning
Authors
Series Title
Studies in Computational Intelligence
Series Volume
800
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-02384-3
DOI
10.1007/978-3-030-02384-3
Hardcover ISBN
978-3-030-02383-6
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XXIX, 423
Number of Illustrations
49 b/w illustrations, 90 illustrations in colour
Topics