Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (5 chapters)
Keywords
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.
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Authors and Affiliations
Bibliographic Information
Book Title: Interpretability of Computational Intelligence-Based Regression Models
Authors: Tamás Kenesei, János Abonyi
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-21942-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2015
Softcover ISBN: 978-3-319-21941-7Published: 10 November 2015
eBook ISBN: 978-3-319-21942-4Published: 22 October 2015
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: X, 82
Number of Illustrations: 20 b/w illustrations, 14 illustrations in colour
Topics: Artificial Intelligence, Computational Intelligence, Data Mining and Knowledge Discovery