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Twin Support Vector Machines: Models, Extensions and Applications: Studies in Computational Intelligence, cartea 659

Autor Jayadeva, Reshma Khemchandani, Suresh Chandra
en Limba Engleză Hardback – 24 oct 2016
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.
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Specificații

ISBN-13: 9783319461847
ISBN-10: 3319461842
Pagini: 250
Ilustrații: XIV, 211 p. 21 illus., 20 illus. in color.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.5 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

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

Textul de pe ultima copertă

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.

Caracteristici

Presents models, extensions, and applications of twin support vector machines (TWSVM) Offers a systematic and focused study of the various aspects of TWSVM and related developments for classification and regression Discusses most of the basic models of TWVSM as well as important and challenging applications of the tools Includes a chapter on "Additional Topics" to discuss kernel optimization and support tensor machine topics Includes supplementary material: sn.pub/extras