Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Using diversity measures for generating error-correcting output codes in classifier ensembles
Pattern Recognition Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Error-correcting output codes: a general method for improving multiclass inductive learning programs
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
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Error Correction Output Codes (ECOC) can improve generalization performance when applied to multiclass problems. In this paper, we compared various criteria used to design codematrices. We also investigated how loss functions affect the results of ECOC. We found that there was no clear evidence of difference between the various criteria used to design codematrices. The One Per Class (OPC) codematrix with Hamming loss yields a higher error rate. The error rate from margin based decoding is lower than from Hamming decoding. Some comments on ECOC are made, and its efficacy is investigated through empirical study.