An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Stochastic Organization of Output Codes in Multiclass Learning Problems
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECOC-ONE: A Novel Coding and Decoding Strategy
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Blurred Shape Model for binary and grey-level symbol recognition
Pattern Recognition Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On the Decoding Process in Ternary Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error-Correcting Ouput Codes Library
The Journal of Machine Learning Research
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Primitive segmentation in old handwritten music scores
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
A genetic inspired optimization for ECOC
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
On the design of an ECOC-Compliant Genetic Algorithm
Pattern Recognition
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The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.