Twin Support Vector Machines : Models, Extensions and Applications

Jayadeva

ISBN 10: 3319461842 / ISBN 13: 9783319461847

Published by Springer, Springer, 2016

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783319461847

Bibliographic Details

• Title: Twin Support Vector Machines : Models, Extensions and Applications
• Publisher: Springer, Springer
• Publication Date: 2016
• Language: English
• ISBN 10: 3319461842
• ISBN 13: 9783319461847
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 18D millimeters 241 millimeters width by 160 millimeters height by 18 millimeters depth
• Weight: 512 grams

About this title

Synopsis

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.