Least Squares and Estimation Measures via Error Correcting Output Code
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Combining Labeled and Unlabeled Data for Text Classification with a Large Number of Categories
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An incremental node embedding technique for error correcting output codes
Pattern Recognition
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
On the Decoding Process in Ternary Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
On the design of an ECOC-Compliant Genetic Algorithm
Pattern Recognition
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Ternary Error-Correcting Output Codes (ECOC), which can unify most of the state-of-the-art decomposition frameworks such as one-versus-one, one-versus-all, sparse coding, dense coding, etc., is considered more flexible to model multiclass classification problems than Binary ECOC. Meanwhile, there are many corresponding decoding strategies that have been proposed for Ternary ECOC in earlier literatures. Note that there is few working by posterior probabilities, which can be considered as a Bayes decision rule and hence obtain a better performance in usual. Passerini et al. (2004) [16] have recently proposed a decoding strategy based on posterior probabilities. However, according to the analyses of this paper, Passerini et al.'s (2004) [16] method suffers some defects and result in bias. To overcome that, we proposed a variation of it by refining the decomposition process of probability to get smoother estimates. Our bias-variance analysis shows that the decrease in error by our variant is due to a decrease in variance. Besides, we extended an efficient method of obtaining posterior probabilities based on the linear rule for decoding process in Binary ECOC to Ternary ECOC. On ten benchmark datasets, we observe that the two decoding strategies based on posterior probabilities in this paper obtain better performance than other ones in earlier references.