Twin Support Vector Machines

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.

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

Inhalt
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