K-SVCR. A support vector machine for multi-class classification
The problem of multi-class classification is usually solved by a decomposing and reconstruction procedure when two-class decision machines are implied. During the decomposing phase, training data are partitioned into two classes in several manners and two-class learning machines are trained. To assign the class for a new entry, machines outputs are evaluated in a specific pulling scheme. This article introduces the Support Vector Classification-Regression machine for K-class classification purposes (K-SVCR), a new training algorithm with ternary outputs {?1,0,+1} based on Vapnik's Support Vector theory. This new machine evaluates all the training data into a 1-versus-1-versus-rest structure during the decomposing phase by using a mixed classification and regression SV Machine (SVM) formulation. For the reconstruction, a specific pulling scheme considering positive and negative votes has been designed, making the overall learning architecture more fault-tolerant as it will be demonstrated.
support vector machines multi-classification robustness