Linear Classification of data with Support Vector Machines and Generalized Support Vector Machines
This work provides a theoretical extension of support vector machines for classification, but appears incremental as it builds directly on existing SVM frameworks.
The authors introduced generalized support vector machines for data classification and proved their equivalence to generalized variational inequality problems, establishing existence results for solutions with supporting examples.
In this paper, we study the support vector machine and introduced the notion of generalized support vector machine for classification of data. We show that the problem of generalized support vector machine is equivalent to the problem of generalized variational inequality and establish various results for the existence of solutions. Moreover, we provide various examples to support our results.