Name: Interpretability of Computational Intelligence-Based Regression Models (Springerbriefs in Computer Science) (en Inglés)
Price: 325.12 PEN
Availability: InStock
Author: Tamás Kenesei; János Abonyi
ISBN: 9783319219417

portada Interpretability of Computational Intelligence-Based Regression Models (Springerbriefs in Computer Science) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Tamás Kenesei; János Abonyi

Año

2015

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9783319219417

N° edición

Categorías

Inteligencia artificial

Interpretability of Computational Intelligence-Based Regression Models (Springerbriefs in Computer Science) (en Inglés)

Tamás Kenesei; János Abonyi (Autor) · Springer · Tapa Blanda

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Interpretability of Computational Intelligence-Based Regression Models (Springerbriefs in Computer Science) (en Inglés)"

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.