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Interpretability of Computational Intelligence-Based Regression Models

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Interpretability of Computational Intelligence-Based Regression Models/ by Tamás Kenesei, János Abonyi.
作者:	Kenesei, Tamás.
其他作者:	Abonyi, János.
面頁冊數:	X, 82 p. 34 illus., 14 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-21942-4
ISBN:	9783319219424

Interpretability of Computational Intelligence-Based Regression Models
Kenesei, Tamás.

Interpretability of Computational Intelligence-Based Regression Models[electronic resource] /by Tamás Kenesei, János Abonyi. - 1st ed. 2015. - X, 82 p. 34 illus., 14 illus. in color.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.

Introduction -- Interpretability of Hinging Hyperplanes -- Interpretability of Neural Networks -- Interpretability of Support Vector Machines -- Summary.

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.

ISBN: 9783319219424

Standard No.: 10.1007/978-3-319-21942-4doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Interpretability of Computational Intelligence-Based Regression Models
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