Separability of ternary Error-Correcting Output Codes

Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Oriol Pujol, Petia Radeva, Sergio Escalera
• Keywords: Computer Vision, ICPR 2008, Ternary Distance Maximization, Ternary Ecoc Distance, Ternary Ecoc Framework