Support Vector Machines : Optimization Based Theory, Algorithms, and Extensions

Naiyang Deng

ISBN 10: 143985792X / ISBN 13: 9781439857922

Edité par Chapman And Hall/CRC, 2013

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - Enabling a sound understanding of SVMs, this book gives readers the tools to solve real-world problems using SVMs. It presents an accessible treatment of the two main components of SVMs-classification problems and regression problems. The authors emphasize the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. They construct SVMs for semi-supervised, knowledge-based, and robust classification problems. They also cover SVMs for Universum, privileged, multi-class, multi-instance, and multi-label classification problems.

N° de réf. du vendeur 9781439857922

Détails bibliographiques

• Titre: Support Vector Machines : Optimization Based Theory, Algorithms, and Extensions
• Éditeur: Chapman And Hall/CRC
• Date de parution: 2013
• Langue: anglais
• ISBN à 10 chiffres: 143985792X
• ISBN à 13 chiffres: 9781439857922
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 240 L x 161 H x 24 P (en millimètres) 240 millimètres de largeur par 161 millimètres de hauteur par 24 millimètres de profondeur
• Poids: 710 grammes

A propos de ce titre

Synopsis

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.

The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations.

To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature.

Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.

À propos de l?auteur

Naiyang Deng, Yingjie Tian, Chunhua Zhang