Twin Support Vector Machines: Models, Extensions and Applications

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 Front Matter....Pages i-xiv Introduction....Pages 1-24 Generalized Eigenvalue Proximal Support Vector Machines....Pages 25-42 Twin Support Vector Machines (TWSVM) for Classification....Pages 43-62 TWSVR: Twin Support Vector Machine Based Regression....Pages 63-101 Variants of Twin Support Vector Machines: Some More Formulations....Pages 103-123 TWSVM for Unsupervised and Semi-supervised Learning....Pages 125-152 Some Additional Topics....Pages 153-192 Applications Based on TWSVM....Pages 193-206 Back Matter....Pages 207-211