Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Ultraconservative Online Algorithms for Multiclass Problems
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
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ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
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Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Subclass Problem-Dependent Design for Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
IEEE Transactions on Neural Networks
Traffic sign recognition system with β -correction
Machine Vision and Applications
Decoding of ternary error correcting output codes
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier.