Von dem Buch Interpretability of Computational Intelligence-Based Regression Models haben wir 2 gleiche oder sehr ähnliche Ausgaben identifiziert!
Falls Sie nur an einem bestimmten Exempar interessiert sind, können Sie aus der folgenden Liste jenes wählen, an dem Sie interessiert sind:
100%: Tamás Kenesei, János Abonyi: Interpretability of Computational Intelligence-Based Regression Models (ISBN: 9783319219424) 2015, Erstausgabe, in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
60%: Tamás Kenesei: Interpretability of Computational Intelligence-Based Regression Models (ISBN: 9783319219417) 2015, Erstausgabe, in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
Interpretability of Computational Intelligence-Based Regression Models
14 Angebote vergleichen
Bester Preis: € 2,59 (vom 21.04.2019)1
Interpretability of Computational Intelligence-Based Regression Models
DE PB NWISBN: 9783319219417 bzw. 3319219413, in Deutsch, Springer Shop, Taschenbuch, neu.
Lieferung aus: Deutschland, Lagernd.
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. Soft 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. Soft cover.
2
Interpretability of Computational Intelligence-Based Regression Models (SpringerBriefs in Computer Science) (2015)
EN PB NW FEISBN: 9783319219417 bzw. 3319219413, in Englisch, 82 Seiten, Springer, Taschenbuch, neu, Erstausgabe.
Neu ab: $41.02 (15 Angebote)
Gebraucht ab: $46.53 (5 Angebote)
Zu den weiteren 20 Angeboten bei Amazon.com
Lieferung aus: Vereinigte Staaten von Amerika, Usually ships in 2-3 business days.
Von Händler/Antiquariat, TOTAL BOOKS.
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., Paperback, Ausgabe: 1st ed. 2015, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2015-10-23, Freigegeben: 2015-11-16, Studio: Springer.
Von Händler/Antiquariat, TOTAL BOOKS.
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., Paperback, Ausgabe: 1st ed. 2015, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2015-10-23, Freigegeben: 2015-11-16, Studio: Springer.
3
Interpretability of Computational Intelligence-Based Regression Models
~EN NW EB DLISBN: 9783319219424 bzw. 3319219421, vermutlich in Englisch, Springer Shop, neu, E-Book, elektronischer Download.
Lieferung aus: Japan, Lagernd, zzgl. Versandkosten.
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. eBook.
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. eBook.
4
Interpretability of Computational Intelligence-Based Regression Models (SpringerBriefs in Computer Science) (2015)
EN NW FE EB DLISBN: 9783319219424 bzw. 3319219421, in Englisch, 82 Seiten, Springer, neu, Erstausgabe, E-Book, elektronischer Download.
Lieferung aus: Vereinigte Staaten von Amerika, E-Book zum Download.
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., Kindle Edition, Ausgabe: 1st ed. 2015, Format: Kindle eBook, Label: Springer, Springer, Produktgruppe: eBooks, Publiziert: 2015-11-16, Freigegeben: 2015-11-16, Studio: Springer.
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., Kindle Edition, Ausgabe: 1st ed. 2015, Format: Kindle eBook, Label: Springer, Springer, Produktgruppe: eBooks, Publiziert: 2015-11-16, Freigegeben: 2015-11-16, Studio: Springer.
5
/ Abonyi | Interpretability of Computational Intelligence-Based Regression Models | Springer | 1st ed. 2015 | 2015
DE NWISBN: 9783319219417 bzw. 3319219413, in Deutsch, Springer, neu.
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.
6
Interpretability of Computational Intelligence-Based Regression Models
DE NWISBN: 9783319219417 bzw. 3319219413, in Deutsch, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Lieferzeit: 11 Tage, zzgl. Versandkosten.
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.
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.
7
Interpretability of Computational Intelligence-Based Regression Models (2015)
EN NW EB DLISBN: 9783319219424 bzw. 3319219421, in Englisch, Springer, Springer, Springer, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigte Staaten von Amerika, in-stock.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
8
Interpretability of Computational Intelligence-Based Regression Models (2015)
~DE PB NWISBN: 9783319219424 bzw. 3319219421, vermutlich in Deutsch, Springer International Publishing, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
10
Interpretability of Computational Intelligence (2015)
DE PB NWISBN: 9783319219417 bzw. 3319219413, in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Lade…