A probabilistic tri-class Support Vector Machine
Rights accessOpen Access
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.
c 2010 JPRR. All rights reserved. Permissions to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or to republish, requires a fee and/or special permission from JPRR. La publicació original està disponible en www.jprr.org
CitationGonzález, L. [et al.]. A probabilistic tri-class Support Vector Machine. "Journal of pattern recognition research", Juliol 2010, vol. 5, núm. 1, p. 1-9.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder