A Probabilistic Tri-class Support Vector Machine
Luis Gonzalez-Abril, Cecilio Angulo, Francisco Velasco and Juan Antonio Ortega
Journal of Pattern Recognition Research
A probabilistic interpretation for the output obtained from a tri-class Support Vector Machine into a multi-classification problem is presented in this paper. Probabilistic outputs are defined when solving a multi-class problem by using an ensemble architecture with tri-class learning machines working in parallel. This architecture enables the definition of an ‘interpretation’ mapping which works on signed and probabilistic outputs providing more control to the user on the classification problem.