Using support vector machines in fuzzy classification
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Using support vector machines in fuzzy classification
DE NW
ISBN: 9783659678868 bzw. 3659678864, in Deutsch, neu.
Lieferung aus: Deutschland, zzgl. Versandkosten.
Fuzzy classification is one of methods used for pattern classification, which is germane to many engineering applications. An output from fuzzy classification is an assignment of patterns to fuzzy classes. There are several methods for fuzzy classification; in this paper we propose a new method based on the soft margin support vector machines classifier (C-SVM). These classifiers are based on statistical learning theory and they are widely used in pattern classification. In our approach, the decision boundary and slack variables obtained from C-SVM are used for the definition of a new optimization problem. The goal is to find an optimal parametrized transformation function T , which transforms the distance of a pattern from de-cision boundary to its membership degree. Quadratic programming is used to find suitable values for the parameters of T . The development of the full scope of this new fuzzy classification method is still in progress.
Fuzzy classification is one of methods used for pattern classification, which is germane to many engineering applications. An output from fuzzy classification is an assignment of patterns to fuzzy classes. There are several methods for fuzzy classification; in this paper we propose a new method based on the soft margin support vector machines classifier (C-SVM). These classifiers are based on statistical learning theory and they are widely used in pattern classification. In our approach, the decision boundary and slack variables obtained from C-SVM are used for the definition of a new optimization problem. The goal is to find an optimal parametrized transformation function T , which transforms the distance of a pattern from de-cision boundary to its membership degree. Quadratic programming is used to find suitable values for the parameters of T . The development of the full scope of this new fuzzy classification method is still in progress.
2
Using support vector machines in fuzzy classification
~EN NW AB
ISBN: 9783659678868 bzw. 3659678864, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Schweiz, Lieferzeit: 2 Tage, zzgl. Versandkosten.
Fuzzy classification is one of methods used for pattern classification, which is germane to many engineering applications. An output from fuzzy classification is an assignment of patterns to fuzzy classes. There are several methods for fuzzy classification, in this paper we propose a new method based on the soft margin support vector machines classifier (C-SVM). These classifiers are based on statistical learning theory and they are widely used in pattern classification. In our approach, the decision boundary and slack variables obtained from C-SVM are used for the definition of a new optimization problem. The goal is to find an optimal parametrized transformation function T , which transforms the distance of a pattern from de-cision boundary to its membership degree. Quadratic programming is used to find suitable values for the parameters of T . The development of the full scope of this new fuzzy classification method is still in progress.
Fuzzy classification is one of methods used for pattern classification, which is germane to many engineering applications. An output from fuzzy classification is an assignment of patterns to fuzzy classes. There are several methods for fuzzy classification, in this paper we propose a new method based on the soft margin support vector machines classifier (C-SVM). These classifiers are based on statistical learning theory and they are widely used in pattern classification. In our approach, the decision boundary and slack variables obtained from C-SVM are used for the definition of a new optimization problem. The goal is to find an optimal parametrized transformation function T , which transforms the distance of a pattern from de-cision boundary to its membership degree. Quadratic programming is used to find suitable values for the parameters of T . The development of the full scope of this new fuzzy classification method is still in progress.
3
Using support vector machines in fuzzy classification
~EN PB NW
ISBN: 9783659678868 bzw. 3659678864, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Using support vector machines in fuzzy classification: Fuzzy classification is one of methods used for pattern classification, which is germane to many engineering applications. An output from fuzzy classification is an assignment of patterns to fuzzy classes. There are several methods for fuzzy classification in this paper we propose a new method based on the soft margin support vector machines classifier (C-SVM). These classifiers are based on statistical learning theory and they are widely used in pattern classification. In our approach, the decision boundary and slack variables obtained from C-SVM are used for the definition of a new optimization problem. The goal is to find an optimal parametrized transformation function T , which transforms the distance of a pattern from de-cision boundary to its membership degree. Quadratic programming is used to find suitable values for the parameters of T . The development of the full scope of this new fuzzy classification method is still in progress. Englisch, Taschenbuch.
Using support vector machines in fuzzy classification: Fuzzy classification is one of methods used for pattern classification, which is germane to many engineering applications. An output from fuzzy classification is an assignment of patterns to fuzzy classes. There are several methods for fuzzy classification in this paper we propose a new method based on the soft margin support vector machines classifier (C-SVM). These classifiers are based on statistical learning theory and they are widely used in pattern classification. In our approach, the decision boundary and slack variables obtained from C-SVM are used for the definition of a new optimization problem. The goal is to find an optimal parametrized transformation function T , which transforms the distance of a pattern from de-cision boundary to its membership degree. Quadratic programming is used to find suitable values for the parameters of T . The development of the full scope of this new fuzzy classification method is still in progress. Englisch, Taschenbuch.
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Using support vector machines in fuzzy classification (Paperback) (2015)
DE PB NW RP
ISBN: 9783659678868 bzw. 3659678864, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu, Nachdruck.
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Language: English Brand New Book ***** Print on Demand *****.
Von Händler/Antiquariat, The Book Depository EURO [60485773], Slough, United Kingdom.
Language: English Brand New Book ***** Print on Demand *****.
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Using support vector machines in fuzzy classification
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ISBN: 9783659678868 bzw. 3659678864, in Englisch, OmniScriptum GmbH & Co. KG, OmniScriptum GmbH & Co. KG, OmniScriptum GmbH & Co. KG, gebraucht.
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Vyoral Zden k,Paperback, English-language edition, Pub by OmniScriptum GmbH & Co. KG.
Vyoral Zden k,Paperback, English-language edition, Pub by OmniScriptum GmbH & Co. KG.
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Using support vector machines in fuzzy classification
~EN PB NW
ISBN: 3659678864 bzw. 9783659678868, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
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Using support vector machines in fuzzy classification (2015)
~EN PB NW
ISBN: 9783659678868 bzw. 3659678864, vermutlich in Englisch, 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
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