Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Information Theory: Coding Theorems for Discrete Memoryless Systems
Information Theory: Coding Theorems for Discrete Memoryless Systems
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning 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
Recursive ECOC for microarray data classification
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A recursive approach to low complexity codes
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Design of reject rules for ECOC classification systems
Pattern Recognition
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The construction of ECOC (Error Correcting Output Coding) classifiers from one or more constituent ECOC classifiers is proposed. Aiming to boost the accuracy of the overall ECOC system, constituent ECOC classifiers are allowed to exchange information via shared binary classifiers. A novel decoding algorithm that iteratively combines binary predictions from constituent ECOC classifiers is introduced for this purpose. Aiming to minimize the degrading effects of dependency between binary predictions, the use of sparsely connected ECOC classifiers of small size is recommended. A comprehensive experimental work shows that competitive ECOC classifiers of size at most @?3.log"2M@? can be obtained in this way.