A Probabilistic Tri-Class Support Vector Machine
Luis Gonzalez-Abril, Cecilio Angulo, Francisco Velasco, Juan Antonio Ortega
Abstract
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 onthe classification problem.