Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning Support Vectors for Face Verification and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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
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Since facial images are affected by various factors, the representation capacity for face database is limited by the prototypes collected for training. Therefore, to extend the capacity covering variations of facial images, we should adopt a complex classifier. It is desirable to use output coding method by considering the number of classes changes. We propose new output coding methods and then compare them with representative conventional output coding methods to investigate the properties of decomposition schemes through the experiment on the ORL face dataset. Finally, we give discussions on some factors that should be considered in the designing of decomposition scheme, to provide some foundation for designing new output coding methods in face recognition.