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100%: M. N. Murty, Rashmi Raghava: Support Vector Machines and Perceptrons (ISBN: 9783319410630) 2016, Erstausgabe, in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
92%: Murty, M. N.; Raghava, Rashmi: Support Vector Machines and Perceptrons Learning, Optimization, Classification, and Application to Social Networks (ISBN: 9783319410623) 2016, in Deutsch, Taschenbuch.Nur diese Ausgabe anzeigen…
Support Vector Machines and Perceptrons - 14 Angebote vergleichen
| Preise | 2016 | 2017 | 2019 |
|---|---|---|---|
| Schnitt | € 45,90 | € 45,17 | € 42,47 |
| Nachfrage |
1
Support Vector Machines and Perceptrons
DE NW EB DLISBN: 9783319410630 bzw. 3319410636, in Deutsch, Springer Shop, neu, E-Book, elektronischer Download.
Lieferung aus: Italien, Lagernd, zzgl. Versandkosten.
This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>, eBook.
This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>, eBook.
2
Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks (SpringerBriefs in Computer Science) (2016)
EN NW FE EB DLISBN: 9783319410630 bzw. 3319410636, in Englisch, 95 Seiten, Springer, neu, Erstausgabe, E-Book, elektronischer Download.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Ebook за изтегляне, Безплатна доставка.
This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>, Kindle Edition, Издание: 1st ed. 2016, Формат: Kindle eBook, Етикет: Springer, Springer, Продуктова група: eBooks, Публикувани: 2016-08-16, Дата на издаване: 2016-08-16, Студио: Springer, Продажбата ранг: 1514778.
This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>, Kindle Edition, Издание: 1st ed. 2016, Формат: Kindle eBook, Етикет: Springer, Springer, Продуктова група: eBooks, Публикувани: 2016-08-16, Дата на издаване: 2016-08-16, Студио: Springer, Продажбата ранг: 1514778.
3
Support Vector Machines and Perceptrons (2016)
DE NW EBISBN: 9783319410630 bzw. 3319410636, in Deutsch, Springer, neu, E-Book.
Lieferung aus: Schweiz, Sofort per Download lieferbar.
Learning, Optimization, Classification, and Application to Social Networks, This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>, PDF, 16.08.2016.
Learning, Optimization, Classification, and Application to Social Networks, This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>, PDF, 16.08.2016.
4
Support Vector Machines and Perceptrons (2016)
DE NW EB DLISBN: 9783319410630 bzw. 3319410636, in Deutsch, Springer, Springer, Springer, neu, E-Book, elektronischer Download.
Lieferung aus: Frankreich, in-stock.
This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maxim.
This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maxim.
5
Support Vector Machines and Perceptrons (2016)
DE PB NWISBN: 9783319410630 bzw. 3319410636, in Deutsch, Springer International Publishing, Taschenbuch, neu.
Support Vector Machines and Perceptrons ab 55.99 € als pdf eBook: Learning, Optimization, Classification, and Application to Social Networks SpringerBriefs in Computer Science. 1st ed. 2016. Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Computer & Internet,.
6
Support Vector Machines and Perceptrons (2016)
DE NW EB DLISBN: 9783319410630 bzw. 3319410636, in Deutsch, neu, E-Book, elektronischer Download.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
7
Support Vector Machines and Perceptrons - Learning, Optimization, Classification, and Application to Social Networks
DE PB NWISBN: 9783319410623 bzw. 3319410628, in Deutsch, Springer-Verlag Gmbh, Taschenbuch, neu.
Lieferung aus: Deutschland, 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
8
Support Vector Machines and Perceptrons
DE NWISBN: 9783319410623 bzw. 3319410628, in Deutsch, neu.
Lieferung aus: Deutschland, Lieferzeit: 11 Tage.
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
9
Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Net
DE USISBN: 3319410628 bzw. 9783319410623, in Deutsch, Springer, gebraucht.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd.
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
10
Support Vector Machines And Perceptrons: Learning, Optimization, Classification, And Application To Social Networks
DE NWISBN: 9783319410623 bzw. 3319410628, in Deutsch, Springer International Publishing, neu.
Lieferung aus: Kanada, Lagernd, zzgl. Versandkosten.
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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