SpringerBriefs in Computer Science

Interpretability of Computational Intelligence-Based Regression Models

Authors: Kenesei, Tamás, Abonyi, János

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  • Authors provide related Matlab code for download
  • Valuable for researchers, graduate students and practitioners in computational intelligence and machine learning
  • Real-world examples drawn from process engineering
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eBook 44,02 €
price for Spain (gross)
  • ISBN 978-3-319-21942-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 57,19 €
price for Spain (gross)
  • ISBN 978-3-319-21941-7
  • 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

The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression.

The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning.

Reviews

“This book is very inspiring and provides many detailed motivating examples after each algorithm discussed. This helps theoretically oriented readers to understand the application scenarios, and helps applied readers to better understand the details and power of the algorithms. The book also provides four sections of useful appendixes on cross validation, orthogonal least squares, a model of the pH process, and a model of an electrical water heater.” (Xin Guo, Mathematical Reviews, September, 2017)

Table of contents (5 chapters)

  • Introduction

    Kenesei, Tamás (et al.)

    Pages 1-8

    Preview Buy Chapter 30,19 €
  • Interpretability of Hinging Hyperplanes

    Kenesei, Tamás (et al.)

    Pages 9-32

    Preview Buy Chapter 30,19 €
  • Interpretability of Neural Networks

    Kenesei, Tamás (et al.)

    Pages 33-48

    Preview Buy Chapter 30,19 €
  • Interpretability of Support Vector Machines

    Kenesei, Tamás (et al.)

    Pages 49-60

    Preview Buy Chapter 30,19 €
  • Summary

    Kenesei, Tamás (et al.)

    Pages 61-63

    Preview Buy Chapter 30,19 €

Buy this book

eBook 44,02 €
price for Spain (gross)
  • ISBN 978-3-319-21942-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 57,19 €
price for Spain (gross)
  • ISBN 978-3-319-21941-7
  • 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
Interpretability of Computational Intelligence-Based Regression Models
Authors
Series Title
SpringerBriefs in Computer Science
Copyright
2015
Publisher
Springer International Publishing
Copyright Holder
The Author(s)
eBook ISBN
978-3-319-21942-4
DOI
10.1007/978-3-319-21942-4
Softcover ISBN
978-3-319-21941-7
Series ISSN
2191-5768
Edition Number
1
Number of Pages
X, 82
Number of Illustrations
20 b/w illustrations, 14 illustrations in colour
Topics