Interpretability of Computational Intelligence-Based Regression Models

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 w...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kenesei, Tamás (Autor), Abonyi, János (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:SpringerBriefs in Computer Science,
Temas:
Artificial intelligence.
Computational intelligence.
Data mining.
Artificial Intelligence.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Acceso en línea:Texto Completo

MARC

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100 1 |a Kenesei, Tamás.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Interpretability of Computational Intelligence-Based Regression Models  |h [electronic resource] /  |c by Tamás Kenesei, János Abonyi. 
250 |a 1st ed. 2015. 
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300 |a X, 82 p. 34 illus., 14 illus. in color.  |b online resource. 
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505 0 |a Introduction -- Interpretability of Hinging Hyperplanes -- Interpretability of Neural Networks -- Interpretability of Support Vector Machines -- Summary. 
520 |a 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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650 0 |a Computational intelligence. 
650 0 |a Data mining. 
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650 2 4 |a Data Mining and Knowledge Discovery. 
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