Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
The Error-Correcting Output Codes (ECOC) is a representative approach of the binary ensemble classifiers for solving multi-class problems. There have been so many researches on an output coding method built on an ECOC foundation. In this paper, we revisit representative conventional ECOC methods in an overlapped learning viewpoint. For this purpose, we propose new OPC based output coding methods in the ECOC point of view, and define a new measure to describe their properties. From the experiment on a face recognition domain, we investigate whether a problem complexity is more important than the overlapped learning or an error correction concept.