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Interpretability of computational intelligence-based regression models[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.536
書名/作者:	Interpretability of computational intelligence-based regression models/ by Tamas Kenesei, Janos Abonyi.
作者:	Kenesei, Tamas.
其他作者:	Abonyi, Janos.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	x, 82 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Regression analysis.
標題:	Computer Science.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Computational Intelligence.
標題:	Data Mining and Knowledge Discovery.
ISBN:	9783319219424
ISBN:	9783319219417
內容註:	Introduction -- Interpretability of Hinging Hyperplanes -- Interpretability of Neural Networks -- Interpretability of Support Vector Machines -- Summary.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-21942-4

Interpretability of computational intelligence-based regression models[electronic resource] /
Kenesei, Tamas.

Interpretability of computational intelligence-based regression models[electronic resource] /by Tamas Kenesei, Janos Abonyi. - Cham :Springer International Publishing :2015. - x, 82 p. :ill. (some col.), digital ;24 cm. - 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:

186625
Regression analysis.

LC Class. No.: QA278.2

Dewey Class. No.: 519.536

Interpretability of computational intelligence-based regression models[electronic resource] /
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