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
IEEE Transactions on Pattern Analysis and Machine Intelligence
An incremental node embedding technique for error correcting output codes
Pattern Recognition
Separability of ternary codes for sparse designs of error-correcting output codes
Pattern Recognition Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Minimal design of error-correcting output codes
Pattern Recognition Letters
Error-correcting output codes based ensemble feature extraction
Pattern Recognition
An IVUS image-based approach for improvement of coronary plaque characterization
Computers in Biology and Medicine
Efficient discriminative learning of class hierarchy for many class prediction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.