Interpretability of Computational Intelligence-Based Regression Models
Language: English
Published by Springer International Publishing, 2015
Series: Book 189 of 322 - SpringerBriefs in Computer Science
- Softcover
- New
Condition: New
£ 42.09
Quantity: Over 20 available
Add to basketItem description from seller
Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Authors provide related Matlab code for downloadValuable for researchers, graduate students and practitioners in computational intelligence and machine learningReal-world examples drawn from process engineeringThe key idea of this book .
Seller Inventory # 36144726
- Title
- Interpretability of Computational Intelligence-Based Regression Models
- Author
- Tamás Kenesei|János Abonyi
- Publisher
- Springer International Publishing
- Publication year
- 2015
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3319219413
- ISBN 13
- 9783319219417
- Series
- Book 189 of 322: SpringerBriefs in Computer Science
- Seller catalogs
- Mathematik/Naturwissenschaften/Technik/Medizin
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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