
Twin Support Vector Machines
Models, Extensions and Applications
Springer (Publisher)
Published on 24. October 2016
Book
Hardback
XIV, 211 pages
978-3-319-46184-7 (ISBN)
Description
This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on "Additional Topics" has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.
More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
20 farbige Abbildungen, 1 s/w Abbildung
XIV, 211 p. 21 illus., 20 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 18 mm
Weight
512 gr
ISBN-13
978-3-319-46184-7 (9783319461847)
DOI
10.1007/978-3-319-46186-1
Schweitzer Classification
Other editions
Additional editions

Jayadeva | Reshma Khemchandani | Suresh Chandra
Twin Support Vector Machines
Models, Extensions and Applications
Book
07/2018
Springer
€106.99
Shipment within 10-15 days

Jayadeva | Reshma Khemchandani | Suresh Chandra
Twin Support Vector Machines
Models, Extensions and Applications
E-Book
10/2016
Springer
€96.29
Available for download
Persons
Content
Introduction.- Generalized Eigenvalue Proximal Support Vector Machines.- Twin Support Vector Machines (TWSVM) for Classification.- TWSVR: Twin Support Vector Machine Based Regression.- Variants of Twin Support Vector Machines: Some More Formulations.- TWSVM for Unsupervised and Semi-Supervised Learning.- Some Additional Topics.- Applications Based on TWSVM.- References