•  Retrait gratuit dans votre magasin Club
  •  7.000.000 titres dans notre catalogue
  •  Payer en toute sécurité
  •  Toujours un magasin près de chez vous     
  •  Retrait gratuit dans votre magasin Club
  •  7.000.000 titres dans notre catalogue
  •  Payer en toute sécurité
  •  Toujours un magasin près de chez vous

Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks

M N Murty, Rashmi Raghava
Livre broché | Anglais | Springerbriefs in Computer Science
52,95 €
+ 105 points
Livraison sous 1 à 4 semaines
Passer une commande en un clic
Payer en toute sécurité
Livraison en Belgique: 3,99 €
Livraison en magasin gratuite

Description

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.>

Spécifications

Parties prenantes

Auteur(s) :
Editeur:

Contenu

Nombre de pages :
95
Langue:
Anglais
Collection :

Caractéristiques

EAN:
9783319410623
Date de parution :
25-08-16
Format:
Livre broché
Format numérique:
Trade paperback (VS)
Dimensions :
156 mm x 234 mm
Poids :
167 g

Les avis